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This study aimed to evaluate the efficacy and safety of intensity modulated radiation therapy (IMRT) for pleural recurrence of thymoma that was not suitable for surgery and had progressed after chemotherapy.
There is a paucity of data on the rates of ipsilateral breast tumor recurrence (IBTR) in BRCA1/2–associated breast cancer (BC). Scarcer yet are outcomes data in BRCA1/2 mutation carriers in the setting of newer mastectomy techniques, such as skin-sparing mastectomies (SSM) and nipple-sparing mastectomies (NSM).
Decreased peripheral lymphocyte counts are associated with survival after radiation therapy (RT) in several solid tumors, although they appear late during or after the radiation course and often correlate with other clinical factors. Here we investigate if absolute lymphocyte counts (ALCs) are independently associated with recurrence in pediatric medulloblastoma early during RT.
Reirradiation for locally recurrent nasopharyngeal carcinoma (NPC) is challenging because prior radiation dose delivered in the first course is often close to the tolerance limit of surrounding normal structures. A delicate balance between achieving local salvage and minimizing treatment toxicities is needed. However, high-level evidence is lacking because available reports are mostly retrospective studies on small series of patients. Pragmatic consensus guidelines, based on an extensive literature search and the pooling of opinions by leading specialists, will provide a useful reference to assist decision-making for these difficult decisions.
Emerging evidence has linked glioblastoma multiforme (GBM) recurrence and survival to stem cell niches (SCNs). However, the traditional tumor–ventricle distance is insufficiently powered for an accurate prediction. We aimed to use a novel inverse distance map for improved prediction.
This study aimed to identify high-risk factors for regional recurrence in patients with breast cancer with pathologically negative lymph nodes.
To estimate the prevalence of and characteristics associated with fear of cancer recurrence (FCR) among human papillomavirus (HPV)-associated oropharyngeal cancer (OPC) survivors.
The HD16 trial of the German Hodgkin Study Group (NCT00736320) demonstrated that radiation therapy in early-stage Hodgkin lymphoma without risk factors cannot be safely omitted, and therefore combined modality therapy (CMT) remains the standard treatment. To demonstrate the local effect of consolidating involved-field radiation therapy (IF-RT), we performed an analysis of the recurrence pattern of positron emission tomography (PET)-negative HD16 patients.
Brain metastases represent a major indication for stereotactic radiosurgery (SRS), but further study is needed regarding repeat SRS (SRS2) after local or marginal recurrence after prior SRS (SRS1). We report local tumor control (LC) after SRS2 and identify predictors of radiation necrosis (RN) and symptomatic RN (SRN).
Higher energy (>6 MV) photons reduce dose inhomogeneity with breast tangent beams, thereby reducing late breast toxicity, but skin and superficial tissue sparing by higher energy beams raises concerns about local recurrence (LR) risk. This study aimed to determine whether beam energy and surgical bed-to-skin distance affect LR.
Randomized clinical trials have shown that regional nodal irradiation (RNI) in patients with unselected N1 breast cancer improves breast cancer-specific survival. However, the benefit of RNI in women with biologically low-risk N1 breast cancer is uncertain. We conducted a population-based study to determine whether RNI is associated with improved breast cancer recurrence-free interval (BCRFI) in this population.
The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal sequence of the protein defines the more than 200 M types of Strep A and also contains epitopes that elicit opsonic antibodies, some of which cross-react with heterologous M types. Current efforts to develop broadly protective M protein-based vaccines are directed at identifying potential cross-protective epitopes located in the N-terminal regions of cluster-related M proteins for use as vaccine antigens. In this study, we have used a comprehensive approach using the recurrent neural network ABCpred and IEDB epitope conservancy analysis tools to predict 16 residue linear B-cell epitopes from 117 clinically relevant M types of Strep A (~88% of global Strep A infections). Furthermore, to examine the immunogenicity of these epitope-based vaccines, nine peptides that together shared ≥60% sequence identity with 37 heterologous M proteins were incorporated into two recombinant hybrid protein vaccines, in which the epitopes were repeated 2 or 3 times, respectively. The combined immune responses of immunized rabbits showed that the vaccines elicited significant levels of antibodies against all nine vaccine epitopes present in homologous N-terminal 1–50 amino acid synthetic M peptides, as well as cross-reactive antibodies against 16 of 37 heterologous M peptides predicted to contain similar epitopes. The epitope-specificity of the cross-reactive antibodies was confirmed by ELISA inhibition assays and functional opsonic activity was assayed in HL-60-based bactericidal assays. The results provide important information for the future design of broadly protective M protein-based Strep A vaccines.
Many authoritarian regimes have taken to censoring internet access in order to stop the spread of misinformation, restrict citizens from discussing certain topics, and prevent mobilization, among other reasons. There are several theories about the effectiveness of censorship. Some suggest that censorship will effectively limit the flow of information, whereas others predict that a backlash will form, resulting in ultimately more discussion about the topic. In this work, we analyze the role of communities and gatekeepers during multiple internet outages in Venezuela in January 2019. First, we measure how critical information (e.g., entities and hashtags) spreads during outages focusing on information recurrence and burstiness within and across language and location communities. We discover that information bursts tend to cross both language and location community boundaries rather than being limited to a single community during several outages. Then we identify users who play central roles and propose a novel method to detect gatekeepers—users who prevent critical information from spreading across communities during outages. We show that bilingual and English-speaking users play more central roles compared to Spanish-speaking users, but users inside and outside Venezuela have similar distribution of centrality. Finally, we measure the differences in social network structure before and after each outage event and discuss its effect on how information spreads. We find that with each outage event social connections tend to get less connected with higher mean shortest path, indicating that the effect of censorship makes it harder for information to spread.
Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.
With the emerging penetration of renewables and dynamic loads, the understanding of grid edge loading conditions becomes increasingly substantial. Load modelling researches commonly consist of explicitly expressed load models and non-explicitly expressed techniques, of which artificial intelligence approaches turn out to be the major path. This paper reveals the artificial intelligence-based load modelling technique to enhance the knowledge of current and future load information considering geographical and weather dependencies. This paper presents a recurrent neural network based sequence to sequence (Seq2Seq) model to forecast the short-term power loads. Also, a feature attention mechanism, which is along channel and time directions, is developed to improve the efficiency of feature learning. The experiments over three publicly available datasets demonstrate the accuracy and effectiveness of the proposed model.
In the limit of small trial moves the Metropolis Monte Carlo algorithm is equivalent to gradient descent on the energy function in the presence of Gaussian white noise. This observation was originally used to demonstrate a correspondence between Metropolis Monte Carlo moves of model molecules and overdamped Langevin dynamics, but it also applies in the context of training a neural network: making small random changes to the weights of a neural network, accepted with the Metropolis probability, with the loss function playing the role of energy, has the same effect as training by explicit gradient descent in the presence of Gaussian white noise. We explore this correspondence in the context of a simple recurrent neural network. We also explore regimes in which this correspondence breaks down, where the gradient of the loss function becomes very large or small. In these regimes the Metropolis algorithm can still effect training, and so can be used as a probe of the loss function of a neural network in regimes in which gradient descent struggles. We also show that training can be accelerated by making purposely-designed Monte Carlo trial moves of neural-network weights.
Recent epidemics demonstrate the global threat of Zika virus (ZIKV), a flavivirus transmitted by mosquitoes. Although infection is usually asymptomatic or mild, newborns of infected mothers can display severe symptoms, including neurodevelopmental abnormalities and microcephaly. Given the large-scale spread, symptom severity, and lack of treatment or prophylaxis, a safe and effective ZIKV vaccine is urgently needed. However, vaccine design is complicated by concern that elicited antibodies (Abs) may cross-react with other flaviviruses that share a similar envelope protein, such as dengue virus, West Nile virus, and yellow fever virus. This cross-reactivity may worsen symptoms of a subsequent infection through Ab-dependent enhancement. To better understand the neutralizing Ab response and risk of Ab-dependent enhancement, further information on germline Ab binding to ZIKV and the maturation process that gives rise to potently neutralizing Abs is needed. In this work, we use binding and structural studies to compare mature and inferred-germline Ab binding to envelope protein domain III of ZIKV and other flaviviruses. We show that affinity maturation of the light-chain variable domain is important for strong binding of the recurrent VH3-23/VK1-5 neutralizing Abs to ZIKV envelope protein domain III, and identify interacting residues that contribute to weak, cross-reactive binding to West Nile virus. These findings provide insight into the affinity maturation process and potential cross-reactivity of VH3-23/VK1-5 neutralizing Abs, informing precautions for protein-based vaccines designed to elicit germline versions of neutralizing Abs.
The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017eV up to more than 1020eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.