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Vaccines have revolutionised the field of medicine, eradicating and controlling many diseases. Recent pandemic vaccine successes have highlighted the accelerated pace of vaccine development and deployment. Leveraging this momentum, attention has shifted to cancer vaccines and personalised cancer vaccines, aimed at targeting individual tumour-specific abnormalities. The UK, now regarded for its vaccine capabilities, is an ideal nation for pioneering cancer vaccine trials. This article convened experts to share insights and approaches to navigate the challenges of cancer vaccine development with personalised or precision cancer vaccines, as well as fixed vaccines. Emphasising partnership and proactive strategies, this article outlines the ambition to harness national and local system capabilities in the UK; to work in collaboration with potential pharmaceutic partners; and to seize the opportunity to deliver the pace for rapid advances in cancer vaccine technology.
The inverse probability of treatment weighted (IPTW) estimator can be used to make causal inferences under two assumptions: (1) no unobserved confounders (ignorability) and (2) positive probability of treatment and of control at every level of the confounders (positivity), but is vulnerable to bias if by chance, the proportion of the sample assigned to treatment, or proportion of control, is zero at certain levels of the confounders. We propose to deal with this sampling zero problem, also known as practical violation of the positivity assumption, in a setting where the observed confounder is cluster identity, i.e., treatment assignment is ignorable within clusters. Specifically, based on a random coefficient model assumed for the potential outcome, we augment the IPTW estimating function with the estimated potential outcomes of treatment (or of control) for clusters that have no observation of treatment (or control). If the cluster-specific potential outcomes are estimated correctly, the augmented estimating function can be shown to converge in expectation to zero and therefore yield consistent causal estimates. The proposed method can be implemented in the existing software, and it performs well in simulated data as well as with real-world data from a teacher preparation evaluation study.
In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their attributes. The attributes can be responses to survey questionnaires and are often high dimensional. We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation–maximization estimation algorithm to estimate the attribute and person locations in the joint latent space. This methodology allows for effective integration, informative visualization and prediction of social networks and attributes. Using JLSM, we explore the French financial elites based on their social networks and their career, political views and social status. We observe a division in the social circles of the French elites in accordance with the differences in their attributes. We analyze user networks and behaviors in multimodal social media systems like YouTube. A R package “jlsm” is developed to fit the models proposed in this paper and is publicly available from the CRAN repository https://cran.r-project.org/web/packages/jlsm/jlsm.pdf.
Psychological tests often involve item clusters that are designed to solicit responses to behavioral stimuli. The dependency between individual responses within clusters beyond that which can be explained by the underlying trait sometimes reveals structures that are of substantive interest. The paper describes two general classes of models for this type of locally dependent responses. Specifically, the models include a generalized log-linear representation and a hybrid parameterization model for polytomous data. A compact matrix notation designed to succinctly represent the system of complex multivariate polytomous responses is presented. The matrix representation creates the necessary formulation for the locally dependent kernel for polytomous item responses. Using polytomous data from an inventory of hostility, we provide illustrations as to how the locally dependent models can be used in psychological measurement.
High prevalence of long COVID symptoms has emerged as a significant public health concern. This study investigated the associations between three doses of COVID-19 vaccines and the presence of any and ≥3 types of long COVID symptoms among people with a history of SARS-CoV-2 infection in Hong Kong, China. This is a secondary analysis of a cross-sectional online survey among Hong Kong adult residents conducted between June and August 2022. This analysis was based on a sub-sample of 1,542 participants with confirmed SARS-CoV-2 infection during the fifth wave of COVID-19 outbreak in Hong Kong (December 2021 to April 2022). Among the participants, 40.9% and 16.1% self-reported having any and ≥3 types of long COVID symptoms, respectively. After adjusting for significant variables related to sociodemographic characteristics, health conditions and lifestyles, and SARS-CoV-2 infection, receiving at least three doses of COVID-19 vaccines was associated with lower odds of reporting any long COVID symptoms comparing to receiving two doses (adjusted odds ratio [AOR]: 0.69, 95% CI: 0.54, 0.87, P = .002). Three doses of inactivated and mRNA vaccines had similar protective effects against long COVID symptoms. It is important to strengthen the coverage of COVID-19 vaccination booster doses, even in the post-pandemic era.
We generalize Hrushovski’s group configuration theorem to the case where the type of the configuration is generically stable, without assuming tameness of the ambient theory. The properties of generically stable types, which we recall in the second section, enable us to adapt the proof known in the stable context.
OBJECTIVES/GOALS: Xylazine is a strong sedative and fentanyl contaminant which has been increasingly detected in drug overdose deaths in Maryland. The goal of this project is to analyze the demographic characteristics and time trends of xylazine-related overdose deaths (XROD) in Maryland from 2020-2022. METHODS/STUDY POPULATION: This cross-sectional study utilizes the Maryland medical examiner's autopsy reports from 2020-2022. These reports include every death in the state that was investigated by the medical examiner, with demographic and toxicological data showing the presence of various substances at the time of death. An XROD was defined as someone who died from drug overdose and had a positive serum xylazine test at time of death. Demographic characteristics and time trends for XROD were analyzed. Multivariable logistic regression modeled associations between demographic variables and the presence of other substances with XROD. RESULTS/ANTICIPATED RESULTS: A total of 1,509 people died from XROD, of which the mean age was 44.4 years and 73.3% were male. The majority were White (57.6%), 39.2% were Black, and 3.2% identified as another race. Over 99.9% of individuals who died from XROD tested positive for fentanyl. XROD peaked in January 2021 and has been trending downwards since then. Adjusted multivariable logistic regression revealed that White individuals had greater odds of XROD relative to Black individuals (OR=1.22, 95% CI=1.07-1.37), and adults aged 30-45 years had higher odds of XROD relative to adults over age 60 (OR=1.26, 95%CI=1.04-1.54). Individuals who used fentanyl had higher odds of XROD relative to those who did not use fentanyl (OR=327.4, 95%CI=46.0-2331.3). DISCUSSION/SIGNIFICANCE: This study demonstrates that middle age, White race, and fentanyl use are associated with xylazine-related overdose deaths in Maryland. Efforts to reduce xylazine-related mortality in the state should address the unique social and geographic factors that influence substance use in this population.
There has been substantial interest in developing Markov chain Monte Carlo algorithms based on piecewise deterministic Markov processes. However, existing algorithms can only be used if the target distribution of interest is differentiable everywhere. The key to adapting these algorithms so that they can sample from densities with discontinuities is to define appropriate dynamics for the process when it hits a discontinuity. We present a simple condition for the transition of the process at a discontinuity which can be used to extend any existing sampler for smooth densities, and give specific choices for this transition which work with popular algorithms such as the bouncy particle sampler, the coordinate sampler, and the zigzag process. Our theoretical results extend and make rigorous arguments that have been presented previously, for instance constructing samplers for continuous densities restricted to a bounded domain, and we present a version of the zigzag process that can work in such a scenario. Our novel approach to deriving the invariant distribution of a piecewise deterministic Markov process with boundaries may be of independent interest.
Recently released Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) collection 6.1 (C6.1) products are useful for understanding ice–atmosphere interactions over East Antarctica, but their accuracy should be known prior to application. This study assessed Level 2 and Level 3 MODIS C6.1 LST products (MxD11_L2 and MxD11C1) in comparison with the radiance-derived in situ LSTs from 12 weather stations. Significant cloud-related issues were identified in both LST products. By utilizing a stricter filter based on automatic weather station cloud data, despite losing 29.4% of the data, accuracy of MODIS LST was greatly improved. The cloud-screened MODIS LST exhibited cold biases (−5.18 to −0.07°C, and root mean square errors from 2.37 to 6.28°C) than in situ LSTs at most stations, with smaller cold biases at inland stations, but larger ones at coastal regions and the edge of plateau. The accuracy was notably higher during warm periods (October–March) than during cold periods (April–September). The cloud-screened MODIS C6.1 LST did not show significant improvements over C5 (Collection 5) version across East Antarctica. Ice-crystal precipitation occurring during temperature inversions at the surface (Tair-Tsurface) played a crucial role in MODIS LST accuracy on inland plateau. In coastal regions, larger MODIS LST biases were observed when the original measurements were lower.
Evaluation of adult antibiotic order sets (AOSs) on antibiotic stewardship metrics has been limited. The primary outcome was to evaluate the standardized antimicrobial administration ratio (SAAR). Secondary outcomes included antibiotic days of therapy (DOT) per 1,000 patient days (PD); selected antibiotic use; AOS utilization; Clostridioides difficile infection (CDI) cases; and clinicians’ perceptions of the AOS via a survey following the final study phase.
Design:
This 5-year, single-center, quasi-experimental study comprised 5 phases from 2017 to 2022 over 10-month periods between August 1 and May 31.
Setting:
The study was conducted in a 752-bed tertiary care, academic medical center.
Intervention:
Our institution implemented AOSs in the electronic medical record (EMR) for common infections among hospitalized adults.
Results:
For the primary outcome, a statistically significant decreases in SAAR were detected from phase 1 to phase 5 (1.0 vs 0.90; P < .001). A statistically significant decreases were detected in DOT per 1,000 PD (4,884 vs 3,939; P = .001), fluoroquinolone orders (407 vs 175; P < .001), carbapenem orders (147 vs 106; P = .024), and clindamycin orders (113 vs 73; P = .01). No statistically significant change in mean vancomycin orders was detected (991 vs 902; P = .221). A statistically significant decrease in CDI cases was also detected (7.8, vs 2.4; P = .002) but may have been attributable to changes in CDI case diagnosis. Clinicians indicated that the AOSs were easy to use overall and that they helped them select the appropriate antibiotics.
Conclusions:
Implementing AOS into the EMR was associated with a statistically significant reduction in SAAR, antibiotic DOT per 1,000 PD, selected antibiotic orders, and CDI cases.
Background: Antibiotics alone are often insufficient to treat recurrent C. difficile infection (rCDI) because they have no activity against C. difficile spores that germinate within a disrupted microbiome. SER-109, an investigational, oral, microbiome therapeutic comprised of purified Firmicutes spores, was designed to reduce rCDI through microbiome repair. We report an integrated efficacy analysis through week 24 for SER-109 from phase 3 studies, ECOSPOR III and ECOSPOR IV. Methods: ECOSPOR III was a randomized, placebo-controlled phase 3 trial conducted at 56 US or Canadian sites that included 182 participants with ≥2 CDI recurrences, confirmed via toxin EIA testing. Participants were stratified by age (<65 years or ≥65 years) and antibiotic regimen (vancomycin, fidaxomicin) and were randomized 1:1 to placebo or SER-109 groups. ECOSPOR IV was an open-label, single-arm study conducted at 72 US or Canadian sites including 263 participants with rCDI enrolled in 2 cohorts: (1) rollover participants from ECOSPOR III who experienced on-study recurrence diagnosed by toxin EIA (n = 29) and (2) participants with ≥1 CDI recurrence (diagnosed by PCR or toxin EIA), inclusive of the current episode (n = 234). In both studies, the investigational product was administered orally as 4 capsules over 3 consecutive days following symptom resolution after standard-of-care antibiotics. The primary efficacy end point was rCDI (recurrent toxin-positive diarrhea requiring treatment) through week 8. Other end points included CDI recurrence rates and safety through 24 weeks. Results: These 349 participants received at least 1 dose of SER-109 in ECOSPOR III or ECOSPOR IV (mean age 64.2; 68.8% female). Overall, 77 participants (22.1%) enrolled with their first CDI recurrence. Four participants received blinded SER-109 in ECOSPOR III followed by a second dose of open-label SER-109 in ECOSPOR IV. Overall, the proportion of participants who received any dose of SER-109 with rCDI at week 8 was 9.5% (33 of 349; 95% CI, 6.6 %–13.0%), and the CDI recurrence rate remained low through 24 weeks (15.2%, 53 of 349; 95% CI, 11.6%–19.4%), corresponding to sustained clinical response rates of 90.5% (95% CI, 87.0%–93.4%) and 84.8% (95% CI, 80.6%–88.4%), respectively (Fig. 1). Most rollover participants (25 of 29, 86.2%) were from the placebo arm; 13.8% had rCDI by week 8. Conclusions: In this integrated analysis, the rates of rCDI were low and durable in participants who received the investigational microbiome therapeutic SER-109, with sustained clinical response rates of 90.5% and 84.8% at weeks 8 and 24, respectively. These data further support the potential benefit of microbiome repair with SER-109 following antibiotics for rCDI to prevent recurrence in high-risk patients.
Financial support: This study was funded by Seres Therapeutics.
Background:Clostridioides difficile infection (CDI) often recurs in patients aged ≥65 years and those with comorbidities. Clinical trials often exclude patients with history of immunosuppression, malignancy, renal insufficiency, or other comorbidities. In a phase 3 trial (ECOSPOR III), SER-109 was superior to placebo in reducing recurrent CDI (rCDI) risk at week 8 and was well tolerated. We report integrated safety data for SER-109 in a broad patient population through week 24 from phase 3 studies: ECOSPOR III and ECOSPOR IV. Methods: ECOSPOR III was a double-blind, placebo-controlled trial conducted in participants with ≥2 CDI recurrences randomized 1:1 to placebo or SER-109. ECOSPOR IV was an open-label, single-arm study conducted in 263 patients with rCDI enrolled in 2 cohorts: (1) rollover participants from ECOSPOR III with on-study recurrence and (2) participants with ≥1 CDI recurrence, inclusive of the current episode. In both studies, the investigational product was administered as 4 oral capsules over 3 days. Treatment-emergent adverse events (TEAEs) were collected through week 8; serious TEAEs and TEAEs of special interest (ie, bacteremia, abscess, meningitis) were collected through week 24. Results: In total, 349 participants received SER-109 in ECOSPOR III and/or ECOSPOR IV (mean age 64.2; 68.8% female). Chronic diseases included cardiac disease (31.2%), immunocompromised or immunosuppressed (21.2%), diabetes (18.9% ), and renal impairment or failure (13.2%). Overall, 221 (63.3%) of 349 participants who received SER-109 experienced TEAEs through week 24. Most were mild to moderate and gastrointestinal. The most common (>5% of participants) treatment related TEAEs were flatulence, abdominal pain and distension, decreased appetite, constipation, nausea, fatigue, and diarrhea. No participants experienced a treatment-related TEAE leading to study withdrawal. Invasive infections were observed in 28 participants (8%); those with identified pathogens were unrelated to SER-109 species, and all were deemed unrelated to treatment by the investigators. There were 11 deaths (3.2%) and 48 participants (13.8%) with serious TEAEs, none of which were deemed treatment related. There were no clinically important differences in the safety profile across subgroups of sex, race, prior antibiotic regimen, or number of CDI recurrences. No safety signals were observed in participants with renal impairment or failure, diabetes, cardiac disease, or immunocompromised or immunosuppressed individuals. Conclusions: In this integrated analysis of phase 3 trials, SER-109, an investigational microbiome therapeutic, was well tolerated in this vulnerable patient population with prevalent comorbidities. No infections, nor those with identified pathogens, were attributed to SER-109 or product species. This safety profile might be expected because this purified product is composed of spore-forming Firmicutes normally abundant in the healthy microbiome.
Financial support: This study was funded by Seres Therapeutics.
Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALMFL, a successor of KALM, replaced CNL with factual English, which is much less restrictive and requires very little training from users. However, KALMFL has limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALMRA to enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRA achieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRA achieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALMRA by drawing attention to the problems faced by the recently made famous AI, ChatGPT.
Microscopy is a widely used method in biological research to observe the morphology and structure of cells. Amongst the plethora of microscopy techniques, fluorescent labeling with dyes or antibodies is the most popular method for revealing specific cellular organelles. However, fluorescent labeling also introduces new challenges to cellular observation, as it increases the workload, and the process may result in nonspecific labeling. Recent advances in deep visual learning have shown that there are systematic relationships between fluorescent and bright-field images, thus facilitating image translation between the two. In this article, we propose the cross-attention conditional generative adversarial network (XAcGAN) model. It employs state-of-the-art GANs (GANs) to solve the image translation task. The model uses supervised learning and combines attention-based networks to explore spatial information during translation. In addition, we demonstrate the successful application of XAcGAN to infer the health state of translated nuclei from bright-field microscopy images. The results show that our approach achieves excellent performance both in terms of image translation and nuclei state inference.
We present the results of two population surveys conducted 10 years apart (December 2010–February 2011 and December 2020–January 2021) of the Critically Endangered white-headed langur Trachypithecus leucocephalus in the Chongzuo White-Headed Langur National Nature Reserve, Guangxi Province, China. In the first survey, we recorded 818 individuals in 105 groups and 16 solitary adult males. In the second survey, we recorded 1,183 individuals in 128 groups and one solitary adult male. As a result of government policies, poaching for food and traditional medicine is no longer a primary threat to these langurs. However, severe forest loss and fragmentation caused by human activities could limit any future increase of this langur population.
Using interview data from protesters and frontline police, this chapter examines the transition of protest policing, from soft to hard models, amid the recent unrest in Hong Kong. While police-centric explanations in the protest policing literature view police as intentional decision-makers who can choose from a variety of policing strategies, we employ a mixed-embeddedness framework that reveals a number of factors—external to police—that have deprived the Hong Kong Police Force of its capacity to facilitate peaceful protest through soft strategies of communication and negotiation. These include, (1) a legitimacy crisis of governance in Hong Kong (a macrolevel factor), (2) the erosion of police authority within the local political culture (a mesolevel factor), and (3) stylistic changes in police–protester interactions, involving the increased use of masks and collective action frames of identification as victims of police (microlevel factors). Together, these factors inaugurated reaction spirals that led to the end of soft, facilitative protest-policing in Hong Kong.
OBJECTIVES/GOALS: The goal of this study was to develop a clinically applicable technique to increase the precision of in vivo dose monitoring during radiation therapy by mapping the dose deposition and resolving the temporal dose accumulation while the treatment is being delivered in real time. METHODS/STUDY POPULATION: Ironizing radiation acoustic imaging (iRAI) is a novel imaging concept with the potential to map the delivered radiation dose on anatomic structure in real time during external beam radiation therapy without interrupting the clinical workflow. The iRAI system consisted of a custom-designed two-dimensional (2D) matrix transducer array with integrated preamplifier array, driven by a clinic-ready ultrasound imaging platform. The feasibility of iRAI volumetric imaging in mapping dose delivery and real-time monitoring of temporal dose accumulation in a clinical treatment plan were investigated with a phantom, a rabbit model, and a cancer patient. RESULTS/ANTICIPATED RESULTS: The total dose deposition and temporal dose accumulation in 3D space of a clinical C-shape treatment plan in a targeted region were first imaged and optimized in a phantom. Then, semi-quantitative iRAI measurements were achieved in an in vivo rabbit model. Finally, for the first time, real-time visualization of radiation dose delivered deep in a patient with liver metastases was performed with a clinical linear accelerator. These studies demonstrate the potential of iRAI to monitor and quantify the radiation dose deposition during treatment. DISCUSSION/SIGNIFICANCE: Described here is the pioneering role of an iRAI system in mapping the 3D radiation dose deposition of a complex clinical radiotherapy treatment plan. iRAI offers a cost-effective and practical solution for real-time visualization of 3D radiation dose delivery, potentially leading to personalized radiotherapy with optimal efficacy and safety.
This study was aimed to evaluate the outcomes of patients with large (>2 cm in great diameter) vestibular schwannomas (VSs) treated with hypofractionated stereotactic radiotherapy (HFSRT) compared to small (<2 cm) ones and the impact of debulking surgery prior to radiation for large VSs.
Methods:
Fifty-nine patients with VSs treated with HFSRT (25 Gy in 5 fractions) were evaluated by tumour size and surgical status. Patients were divided based on tumour size: small VSs (n = 42) and large VSs (n = 17). The large group was further divided into the groups of pre-treatment debulking surgery (n = 8) and no surgery (n = 9). Rates of tumour control, brainstem necrosis and neurologic dysfunction were assessed following treatment. Pre-surgical magnetic resonance imaging (MRI) were used to generate hypothetical HFSRT plans to compare the effect of debulking surgery on dosimetry. Normal tissue complication probability (NTCP) modelling was performed to compare toxicity probabilities with and without surgical debulking in large VSs.
Results:
There was no statistical difference of tumour control rate between small and large VSs with 100% for small tumours and 94·1% for large tumours (p = 0·12), respectively. In large VSs patient, the tumour control rate of HFSRT was 100% (8/8) for surgically debulked patients and 89% (8/9) for non-surgically debulked patients (p = 0·35). There were no patients who experienced brainstem necrosis or progression of facial and trigeminal nerve symptoms after HFSRT in the entire groups of patients. Surgical debulking large VSs did not change the maximum point dose of brainstem (p = 0·98), but significantly decreased volumes of VSs and changed the minimum dose to the hottest 0·5 cc of tumour (p = 0·016) as well as the volume receiving at least 23 Gy (p = 0·023). NTCP modelling revealed very low rates (average < 1%) of brainstem toxicity with or without surgical debulking, but there was a significant difference favoring surgery (p < 0·05).
Conclusions:
HFSRT is a safe and effective treatment for both small and large VSs and is a viable option for patients with large VSs who cannot undergo surgery, if NTCP of pre-debulking HFSRT dosimetry is lower.
While quantum accelerometers sense with extremely low drift and low bias, their practical sensing capabilities face at least two limitations compared with classical accelerometers: a lower sample rate due to cold atom interrogation time; and a reduced dynamic range due to signal phase wrapping. In this paper, we propose a maximum likelihood probabilistic data fusion method, under which the actual phase of the quantum accelerometer can be unwrapped by fusing it with the output of a classical accelerometer on the platform. Consequently, the recovered measurement from the quantum accelerometer is used to estimate bias and drift of the classical accelerometer which is then removed from the system output. We demonstrate the enhanced error performance achieved by the proposed fusion method using a simulated 1D accelerometer precision test scenario. We conclude with a discussion on fusion error and potential solutions.
Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability.