scholarly journals Radiotheranostic Agents Targeting Neuroblastoma: State-of-the-Art and Emerging Perspectives

Onco ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 123-139
Author(s):  
Luca Filippi ◽  
Viviana Frantellizzi ◽  
Marko Magdi Abdou Sidrak ◽  
Joana Gorica ◽  
Stefano Scippa ◽  
...  
Keyword(s):  
High Probability ◽  
State Of The Art ◽  
Somatostatin Receptors ◽  
Alpha Particles ◽  
Web Based ◽  
Theranostic Agent ◽  
Preclinical Phase ◽  
Unique Approach ◽  
Theranostic Agents ◽  
Alpha Therapy

Neuroblastoma (NB) represents the most common extracranial tumor of childhood. Prognosis is quite variable, ranging from spontaneous regression to aggressive behavior with wide metastatization, high mortality, and limited therapeutic options. Radiotheranostics combines a radiopharmaceutical pair in a unique approach, suitable both for diagnosis and therapy. For many years, metaiodobenzylguanidine (MIBG), labeled with 123I for imaging or 131I for therapy, has represented the main theranostic agent in NB, since up to 90% of NB incorporates the aforementioned radiopharmaceutical. In recent years, novel theranostic agents hold promise in moving the field of NB radiotheranostics forward. In particular, SarTATE, consisting of octreotate targeting somatostatin receptors, has been applied with encouraging results, with 64Cu-SARTATE being used for disease detection and with 67Cu-SARTATE being used for therapy. Furthermore, recent evidence has highlighted the potential of targeted alpha therapy (TAT) for treating cancer by virtue of alpha particles’ high ionizing density and high probability of killing cells along their track. On this path, 211At-astatobenzylguanidine (MABG) has been developed as a potential agent for TAT and is actually under evaluation in preclinical NB models. In this review, we performed a web-based and desktop literature research concerning radiotheranostic approaches in NB, covering both the radiopharmaceuticals already implemented in clinical practice (i.e.,123/1311-MIBG) and those still in a preliminary or preclinical phase.

scholarly journals Social and Solidarity Economy Web Information Systems

2014 ◽  
Vol 12 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Mariana Curado Malta ◽  
Ana Alice Baptista ◽  
Cristina Parente
Keyword(s):  
Information Systems ◽  
State Of The Art ◽  
Bibliographic Databases ◽  
Sources Of Information ◽  
Solidarity Economy ◽  
Web Based ◽  
Web Information ◽  
The Social ◽  
Application Profile ◽  
Made In

This paper presents the state of the art on interoperability developments for the social and solidarity economy (SSE) community web based information systems (WIS); it also presents a framework of interoperability for the SSE' WIS and the developments made in a research-in-progress PhD project in the last 3 years. A search on the bibliographic databases showed that so far there are no papers on interoperability initiatives on the SSE, so it was necessary to have other sources of information: a preliminary analysis of the WIS that support SSE activities; and interviews with the representatives of some of the world's most important SSE organisations. The study showed that the WIS are still not interoperable yet. In order to become interoperable a group of the SSE community has been developing a Dublin Corre Application Profile to be used by the SSE community as reference and binding to describe their resources. This paper also describes this on-going process.

scholarly journals Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness

2020 ◽  
Vol 34 (04) ◽  
pp. 3858-3865
Author(s):  
Huijie Feng ◽  
Chunpeng Wu ◽  
Guoyang Chen ◽  
Weifeng Zhang ◽  
Yang Ning
Keyword(s):  
Neural Network ◽  
High Probability ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Computationally Efficient ◽  
Base Classifier ◽  
Training Scheme ◽  
Adversarial Training ◽  
Gaussian Perturbation ◽  
Probabilistic Robustness

Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic robustness guarantee against ℓ2 norm bounded adversarial perturbations. However, how to train a good base classifier that is accurate and robust when smoothed has not been fully investigated. In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier. It is computationally efficient and can be implemented in parallel with other empirical defense methods. We discuss how to implement it under both standard (non-adversarial) and adversarial training scheme. At the same time, we also design a new certification algorithm, which can leverage the regularization effect to provide tighter robustness lower bound that holds with high probability. Our extensive experimentation demonstrates the effectiveness of the proposed training and certification approaches on CIFAR-10 and ImageNet datasets.

scholarly journals Production and supply of alpha particles emitting radionuclides for Targeted Alpha Therapy (TAT).

2021 ◽  
pp. jnumed.120.261016
Author(s):  
Valery Radchenko ◽  
Alfred Morgenstern ◽  
Amirreza Jalilian ◽  
Caterina Ramogida ◽  
Cathy S Cutler ◽  
...  
Keyword(s):  
Alpha Particles ◽  
Targeted Alpha Therapy ◽  
Alpha Therapy

scholarly journals Analyzing Compositionality-Sensitivity of NLI Models

2019 ◽  
Vol 33 ◽  
pp. 6867-6874 ◽  
Author(s):  
Yixin Nie ◽  
Yicheng Wang ◽  
Mohit Bansal
Keyword(s):  
Natural Language ◽  
High Probability ◽  
State Of The Art ◽  
Model Performance ◽  
Analysis Tool ◽  
Bag Of Words ◽  
Compositional Semantics ◽  
Sensitivity Testing ◽  
Performance Loss ◽  
Future Work

Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.

Performability Evaluation of Web-Based Services

2012 ◽  
pp. 243-264
Author(s):  
Magnos Martinello ◽  
Mohamed Kaâniche ◽  
Karama Kanoun
Keyword(s):  
Analytical Models ◽  
Travel Agency ◽  
Multiple Sources ◽  
Web Based ◽  
Software Failures ◽  
Traffic Characteristics ◽  
Evaluation Of Performance ◽  
Sensitivity Studies ◽  
Unique Approach ◽  
Modeling Formalisms

The joint evaluation of performance and dependability in a unique approach leads to the notion of performability which usually combines different analytical modeling formalisms (Markov chains, queueing models, etc.) for assessing systems behaviors in the presence of faults. This chapter presents a systematic modeling approach allowing designers of web-based services to evaluate the performability of the service provided to the users. We have developed a multi-level modeling framework for analyzing the user perceived performability. Multiple sources of service unavailability are taken into account, particularly i) hardware and software failures affecting the servers, and ii) performance degradation due to e.g. overload of servers and probability of loss. The main concepts and the feasibility of the proposed framework are illustrated using a web-based travel agency. Various analytical models and sensitivity studies are presented considering different assumptions with respect to users profiles, architecture, faults, recovery strategies, and traffic characteristics.

TH-AB-BRB-02: Enabling Web-Based Treatment Planning Using a State-Of-The-Art Convex Optimization Solver

2015 ◽  
Vol 42 (6Part41) ◽  
pp. 3704-3704 ◽  
Author(s):  
B Ungun ◽  
M Folkerts ◽  
K Bush ◽  
S Boyd ◽  
L Xing
Keyword(s):  
Convex Optimization ◽  
Treatment Planning ◽  
State Of The Art ◽  
Web Based

scholarly journals Input-Aware Implication Selection Scheme Utilizing ATPG for Efficient Concurrent Error Detection

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 258 ◽  
Author(s):  
Abdus Hassan ◽  
Umar Afzaal ◽  
Tooba Arifeen ◽  
Jeong Lee
Keyword(s):  
Error Detection ◽  
High Probability ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Concurrent Error Detection ◽  
Selection Algorithm ◽  
Probability Of Error ◽  
Selection Scheme ◽  
Selection Strategies

Recently, concurrent error detection enabled through invariant relationships between different wires in a circuit has been proposed. Because there are many such implications in a circuit, selection strategies have been developed to select the most valuable implications for inclusion in the checker hardware such that a sufficiently high probability of error detection ( P d e t e c t i o n ) is achieved. These algorithms, however, due to their heuristic nature cannot guarantee a lossless P d e t e c t i o n . In this paper, we develop a new input-aware implication selection algorithm with the help of ATPG which minimizes loss on P d e t e c t i o n . In our algorithm, the detectability of errors for each candidate implication is carefully evaluated using error prone vectors. The evaluation results are then utilized to select the most efficient candidates for achieving optimal P d e t e c t i o n . The experimental results on 15 representative combinatorial benchmark circuits from the MCNC benchmarks suite show that the implications selected from our algorithm achieve better P d e t e c t i o n in comparison to the state of the art. The proposed method also offers better performance, up to 41.10%, in terms of the proposed impact-level metric, which is the ratio of achieved P d e t e c t i o n to the implication count.

scholarly journals Manufacturing systematics and cladistics: state of the art and generic classification

2017 ◽  
Vol 28 (5) ◽  
pp. 655-685 ◽  
Author(s):  
Christen Rose-Anderssen ◽  
James Baldwin ◽  
Keith Ridgway
Keyword(s):  
Expert System ◽  
Observational Data ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
State Of The Art ◽  
Theory Building ◽  
Theory Testing ◽  
Web Based ◽  
Content Type ◽  
Discrete Manufacturing

Purpose The purpose of this paper is to critically evaluate the state of the art of applications of organisational systematics and manufacturing cladistics in terms of strengths and weaknesses and introduce new generic cladistic and hierarchical classifications of discrete manufacturing systems. These classifications are the basis for a practical web-based expert system and diagnostic benchmarking tool. Design/methodology/approach There were two stages for the research methods, with eight re-iterative steps: one for theory building, using secondary and observational data, producing conceptual classifications; the second stage for theory testing and theory development, using quantitative data from 153 companies and 510 manufacturing systems, producing the final factual cladogram. Evolutionary relationships between 53 candidate manufacturing systems, using 13 characters with 84 states, are hypothesised and presented diagrammatically. The manufacturing systems are also organised in a hierarchical classification with 13 genera, 6 families and 3 orders under one class of discrete manufacturing. Findings This work addressed several weaknesses of current manufacturing cladistic classifications which include the lack of an explicit out-group comparison, limited conceptual cladogram development, limited use of characters and that previous classifications are specific to sectors. In order to correct these limitations, the paper first expands on previous work by producing a more generic manufacturing system classification. Second, it describes a novel web-based expert system for the practical application of the discrete manufacturing system. Practical implications The classifications form the basis for a practical web-based expert system and diagnostic benchmarking tool, but also have a novel use in an educational context as it simplifies and relationally organises extant manufacturing system knowledge. Originality/value The research employed a novel re-iterative methodology for both theory building, using observational data, producing the conceptual classification, and through theory testing developing the final factual cladogram that forms the basis for the practical web-based expert system and diagnostic tool.

scholarly journals Multimodal Speaker Diarization Using a Pre-Trained Audio-Visual Synchronization Model

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5163 ◽  
Author(s):  
Ahmad ◽  
Zubair ◽  
Alquhayz ◽  
Ditta
Keyword(s):  
High Probability ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Talk Shows ◽  
Speaker Diarization ◽  
High Confidence ◽  
Effective Technique ◽  
Complex State ◽  
Synchronization Model ◽  
Short Video

Speaker diarization systems aim to find ‘who spoke when?’ in multi-speaker recordings. The dataset usually consists of meetings, TV/talk shows, telephone and multi-party interaction recordings. In this paper, we propose a novel multimodal speaker diarization technique, which finds the active speaker through audio-visual synchronization model for diarization. A pre-trained audio-visual synchronization model is used to find the synchronization between a visible person and the respective audio. For that purpose, short video segments comprised of face-only regions are acquired using a face detection technique and are then fed to the pre-trained model. This model is a two streamed network which matches audio frames with their respective visual input segments. On the basis of high confidence video segments inferred by the model, the respective audio frames are used to train Gaussian mixture model (GMM)-based clusters. This method helps in generating speaker specific clusters with high probability. We tested our approach on a popular subset of AMI meeting corpus consisting of 5.4 h of recordings for audio and 5.8 h of different set of multimodal recordings. A significant improvement is noticed with the proposed method in term of DER when compared to conventional and fully supervised audio based speaker diarization. The results of the proposed technique are very close to the complex state-of-the art multimodal diarization which shows significance of such simple yet effective technique.

Sign in / Sign up

Export Citation Format

Share Document