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Increasing the complexity of solving budgetary allocation (NP-hardness problem) has led a wide range of methods to minimize the costs. Metaheuristics and Linear Programming (LP) are the most optimization in this fields. Therefore, this study provides insights and a deep understanding of the applicability of LP models in industry and how to formulate SM and affine IPM for solving real world linear problems. Moreover, it will present a better way to deal with decision making problems through the development and comparison of the SM and affine IPM to solve LP optimization problem to maximize profit. Finally, to other researchers particularly of similar interests who are undertaking further investigation on this topic, this study can be vital as a secondary source of information and guidance.

Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, and vandalism. While deep learning (DL) has shown excellent performance in this area, existing approaches have struggled to apply DL models across different anomaly tasks without extensive retraining. This repeated retraining is time-consuming, computationally intensive, and unfair. To address this limitation, a new DL framework is introduced in this study, consisting of three key components: transfer learning to enhance feature generalization, model fusion to improve feature representation, and multi-task classification to generalize the classifier across multiple tasks without training from scratch when new task is introduced. The framework's main advantage is its ability to generalize without requiring retraining from scratch for each new task. Empirical evaluations demonstrate the framework's effectiveness, achieving an accuracy of 97.99% on the RLVS dataset (violence detection), 83.59% on the UCF dataset (shoplifting detection), and 88.37% across both datasets using a single classifier without retraining. Additionally, when tested on an unseen dataset, the framework achieved an accuracy of 87.25%. The study also utilizes two explainability tools to identify potential biases, ensuring robustness and fairness. This research represents the first successful resolution of the generalization issue in anomaly detection, marking a significant advancement in the field.

The Tsetlin Machine (TM) has gained significant attention in Machine Learning (ML). By employing logical fundamentals, it facilitates pattern learning and representation, offering an alternative approach for developing comprehensible Artificial Intelligence (AI) with a specific focus on pattern classification in the form of conjunctive clauses. In the domain of Natural Language Processing (NLP), TM is utilised to construct word embedding and describe target words using clauses. To enhance the descriptive capacity of these clauses, we study the concept of Reasoning by Elimination (RbE) in clauses' formulation, which involves incorporating feature negations to provide a more comprehensive representation. In more detail, this paper employs the Tsetlin Machine Auto-Encoder (TM-AE) architecture to generate dense word vectors, aiming at capturing contextual information by extracting feature-dense vectors for a given vocabulary. Thereafter, the principle of RbE is explored to improve descriptivity and optimise the performance of the TM. Specifically, the specificity parameter s and the voting margin parameter T are leveraged to regulate feature distribution in the state space, resulting in a dense representation of information for each clause. In addition, we investigate the state spaces of TM-AE, especially for the forgotten/excluded features. Empirical investigations on artificially generated data, the IMDB dataset, and the 20 Newsgroups dataset showcase the robustness of the TM, with accuracy reaching 90.62\% for the IMDB., Comment: 8 pages, 8 figures

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model's language capability. Additionally, GPT-4o's vision and speech capabilities are tested through image classification and object recognition tasks, as well as accent classification. The multimodal evaluation assesses the model's performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities, excelling in tasks that require few-shot learning. GPT-4o also provides notable improvements in multimodal tasks compared to its predecessors. However, the model shows variability and faces limitations in handling complex and ambiguous inputs, particularly in audio and vision capabilities. This paper highlights the need for more comprehensive benchmarks and robust evaluation frameworks, encompassing qualitative assessments involving human judgment as well as error analysis. Future work should focus on expanding datasets, investigating prompt-based assessment, and enhancing few-shot learning techniques to test the model's practical applicability and performance in real-world scenarios.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Tsetlin machines (TMs) have been successful in several application domains, operating with high efficiency on Boolean representations of the input data. However, Booleanizing complex data structures such as sequences, graphs, images, signal spectra, chemical compounds, and natural language is not trivial. In this paper, we propose a hypervector (HV) based method for expressing arbitrarily large sets of concepts associated with any input data. Using a hyperdimensional space to build vectors drastically expands the capacity and flexibility of the TM. We demonstrate how images, chemical compounds, and natural language text are encoded according to the proposed method, and how the resulting HV-powered TM can achieve significantly higher accuracy and faster learning on well-known benchmarks. Our results open up a new research direction for TMs, namely how to expand and exploit the benefits of operating in hyperspace, including new booleanization strategies, optimization of TM inference and learning, as well as new TM applications., Comment: 9 pages, 17 figures

Simulation-based inference methods that feature correct conditional coverage of confidence sets based on observations that have been compressed to a scalar test statistic require accurate modeling of either the p-value function or the cumulative distribution function (cdf) of the test statistic. If the model of the cdf, which is typically a deep neural network, is a function of the test statistic then the derivative of the neural network with respect to the test statistic furnishes an approximation of the sampling distribution of the test statistic. We explore whether this approach to modeling conditional 1-dimensional sampling distributions is a viable alternative to the probability density-ratio method, also known as the likelihood-ratio trick. Relatively simple, yet effective, neural network models are used whose predictive uncertainty is quantified through a variety of methods.

Metaverse has emerged as a novel technology with the objective to merge the physical world into the virtual world. This technology has seen a lot of interest and investment in recent times from prominent organizations including Facebook which has changed its company name to Meta with the goal of being the leader in developing this technology. Although people in general are excited about the prospects of metaverse due to potential use cases such as virtual meetings and virtual learning environments, there are also concerns due to potential negative consequences. For instance, people are concerned about their data privacy as well as spending a lot of their time on the metaverse leading to negative impacts in real life. Therefore, this research aims to further investigate the public sentiments regarding metaverse on social media. A total of 86565 metaverse-related tweets were used to perform lexicon-based sentiment analysis. Furthermore, various machine and deep learning models with various text features were utilized to predict the sentiment class. The BERT transformer model was demonstrated to be the best at predicting the sentiment categories with 92.6% accuracy and 0.91 F-measure on the test dataset. Finally, the implications and future research directions were also discussed.

Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging. In particular, although ODEs are differentiable and would allow for gradient-based parameter optimization, the nonlinear dynamics of ODEs often lead to many local minima and extreme sensitivity to initial conditions. We therefore propose diffusion tempering, a novel regularization technique for probabilistic numerical methods which improves convergence of gradient-based parameter optimization in ODEs. By iteratively reducing a noise parameter of the probabilistic integrator, the proposed method converges more reliably to the true parameters. We demonstrate that our method is effective for dynamical systems of different complexity and show that it obtains reliable parameter estimates for a Hodgkin-Huxley model with a practically relevant number of parameters.

Intrusion detection systems (IDSs) built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. Although review papers are used the systematic review or simple methods to analyse and criticize the anomaly NIDS works, the current review uses a traditional way as a quantitative description to find current gaps by synthesizing and summarizing the data comparison without considering algorithms performance. This paper presents a systematic and meta-analysis study of AI for network intrusion detection systems (NIDS) focusing on deep learning (DL) and machine learning (ML) approaches in network security. Deep learning algorithms are explained in their structure, and data intrusion network is justified based on an infrastructure of networks and attack types. By conducting a meta-analysis and debating the validation of the DL and ML approach by effectiveness, used dataset, detected attacks, classification task, and time complexity, we offer a thorough benchmarking assessment of the current NIDS-based publications-based systematic approach. The proposed method is considered reviewing works for the anomaly-based network intrusion detection system (anomaly-NIDS) models. Furthermore, the effectiveness of proposed algorithms and selected datasets are discussed for the recent direction and improvements of ML and DL to the NIDS. The future trends for improving an anomaly-IDS for continuing detection in the evolution of cyberattacks are highlighted in several research studies.

The potential of artificial intelligence (AI)-based large language models (LLMs) holds considerable promise in revolutionizing education, research, and practice. However, distinguishing between human-written and AI-generated text has become a significant task. This paper presents a comparative study, introducing a novel dataset of human-written and LLM-generated texts in different genres: essays, stories, poetry, and Python code. We employ several machine learning models to classify the texts. Results demonstrate the efficacy of these models in discerning between human and AI-generated text, despite the dataset's limited sample size. However, the task becomes more challenging when classifying GPT-generated text, particularly in story writing. The results indicate that the models exhibit superior performance in binary classification tasks, such as distinguishing human-generated text from a specific LLM, compared to the more complex multiclass tasks that involve discerning among human-generated and multiple LLMs. Our findings provide insightful implications for AI text detection while our dataset paves the way for future research in this evolving area.

The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task in domains outside physics. In this work, we illustrate the potential of IQNs as components of emulators using the simulation of jets as an example. Specifically, we use an IQN to map jets described by their 4-momenta at the generation level to jets at the event reconstruction level. The conditional densities emulated by our model closely match those generated by $\texttt{Delphes}$, while also enabling faster jet simulation.

High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations without explicit use of the likelihood function. This is of particular interest when the latter is intractable. In this work, we introduce a simple extension of the recently proposed likelihood-free frequentist inference (LF2I) approach that has some computational advantages. Like LF2I, this extension yields provably valid confidence sets in parameter inference problems in which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology.

The emergence of an AI-powered chatbot that can generate human-like sentences and write coherent essays has caught the world's attention. This paper discusses the historical overview of chatbots and the technology behind Chat Generative Pre-trained Transformer, better known as ChatGPT. Moreover, potential applications of ChatGPT in various domains, including healthcare, education, and research, are highlighted. Despite promising results, there are several privacy and ethical concerns surrounding ChatGPT. In addition, we highlight some of the important limitations of the current version of ChatGPT. We also ask ChatGPT to provide its point of view and present its responses to several questions we attempt to answer., Comment: This manuscript has been accepted by Artificial Intelligence and Applications (AIA, ISSN: 2811-0854), https://doi.org/10.47852/bonviewAIA3202939, 2023

In this work, the ferrite nanocomposite CuxCe$_{0.3-X}$Ni$_{0.7}$Fe$_2$O$_4$ is prepared (where: x = 0, 0.05, 0.1, 0.15, 0.2, 0.25) was prepared using the auto combustion technique (sol-gel), and citric acid was utilized as the fuel for Auto combustion. The results of X-ray diffraction (XRD), emitting field scanning electron microscope (FE-SEM), and energy dispersive X-ray analyzer (EDX) tests revealed that the prepared compound has a face-centered cubic structure (FCC) polycrystalline, and the lattice constant increases with an increase in the percentage of doping for the copper ion, and decreases for the cerium ion and that the compound is porous, and its molecules are spherical, and there are no additional elements present other than those used in the synthesis of the compound, indicating that it is of high purity, and the combination has a high sensitivity to Nitrogen dioxide (NO$_2$) gas, as determined by the gas detecting equipment., Comment: 9 pages,5 figures

Digital arts have gained an unprecedented level of popularity with the emergence of non-fungible tokens (NFTs). NFTs are cryptographic assets that are stored on blockchain networks and represent a digital certificate of ownership that cannot be forged. NFTs can be incorporated into a smart contract which allows the owner to benefit from a future sale percentage. While digital art producers can benefit immensely with NFTs, their production is time consuming. Therefore, this paper explores the possibility of using generative adversarial networks (GANs) for automatic generation of digital arts. GANs are deep learning architectures that are widely and effectively used for synthesis of audio, images, and video contents. However, their application to NFT arts have been limited. In this paper, a GAN-based architecture is implemented and evaluated for novel NFT-style digital arts generation. Results from the qualitative case study indicate that the generated artworks are comparable to the real samples in terms of being interesting and inspiring and they were judged to be more innovative than real samples., Comment: Submitted to the 7th International Conference on Machine Learning Technologies

In this work we introduce NWT, an expressive speech-to-video model. Unlike approaches that use domain-specific intermediate representations such as pose keypoints, NWT learns its own latent representations, with minimal assumptions about the audio and video content. To this end, we propose a novel discrete variational autoencoder with adversarial loss, dVAE-Adv, which learns a new discrete latent representation we call Memcodes. Memcodes are straightforward to implement, require no additional loss terms, are stable to train compared with other approaches, and show evidence of interpretability. To predict on the Memcode space, we use an autoregressive encoder-decoder model conditioned on audio. Additionally, our model can control latent attributes in the generated video that are not annotated in the data. We train NWT on clips from HBO's Last Week Tonight with John Oliver. NWT consistently scores above other approaches in Mean Opinion Score (MOS) on tests of overall video naturalness, facial naturalness and expressiveness, and lipsync quality. This work sets a strong baseline for generalized audio-to-video synthesis. Samples are available at https://next-week-tonight.github.io/NWT/.

The increasing demand on the internet trafficking to meet the demands of video streaming and mobile communication has exerted too much pressure. Accordingly, this demand will result in providing high bandwidth which in turn increase the use of cells on the network. However, the current networks do not meet the requirements of the necessary data rate. Therefore, the Dense Wavelength Division Multiplexing (DWDM) network and the Radio over Fiber (RoF) technology are the ideal solution for providing the necessary data rate needed in the current networks. The DWDM, will increase the transmission distance and will increase the data rate; nonetheless, the DWDM network will be compromised especially by the Non-linear effects. This study in intended to propose a system to find solutions for the issues of increasing the data rate and for reducing the nonlinear effects. There are number of technologies that could be adopted to fix such issues, which includes; Optical Phase Conjugation (OPC), Semiconductor Optical Amplifier (SOA), Comment: Dense Wavelength Division Multiplexing (DWDM), Radio over Fiber (RoF), Self-Phase Modulation (SPM), Cross Phase Modulation (XPM), Four Wave Mixing (FWM), Optical Phase Conjugation (OPC), Semiconductor Optical Amplifier (SOA), Digital Signal Processing (DSP), and SCRO (Squared Cosine Roll off)

Sorting has been a profound area for the algorithmic researchers and many resources are invested to suggest more works for sorting algorithms. For this purpose, many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. In this paper we implemented the bubble and merge sort algorithms using Message Passing Interface (MPI) approach. The proposed work tested on two standard datasets (text file) with different size. The main idea of the proposed algorithm is distributing the elements of the input datasets into many additional temporary sub-arrays according to a number of characters in each word. The sizes of each of these sub-arrays are decided depending on a number of elements with the same number of characters in the input array. We implemented MPI using Intel core i7-3610QM ,(8 CPUs),using two approaches (vectors of string and array 3D) . Finally, we get the data structure effects on the performance of the algorithm for that we choice the second approach., Comment: 5 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:1407.6603

Sorting has been a profound area for the algorithmic researchers and many resources are invested to suggest more works for sorting algorithms. For this purpose, many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. In this paper we implemented the bubble sort algorithm using multithreading (OpenMP). The proposed work tested on two standard datasets (text file) with different size . The main idea of the proposed algorithm is distributing the elements of the input datasets into many additional temporary sub-arrays according to a number of characters in each word. The sizes of each of these sub-arrays are decided depending on a number of elements with the same number of characters in the input array. We implemented OpenMP using Intel core i7-3610QM ,(8 CPUs),using two approaches (vectors of string and array 3D) . Finally, we get the data structure effects on the performance of the algorithm for that we choice the second approach., Comment: 4 pages, 5 firgyes

In the transmission of digital data at a relatively high rate over a particular band limited channel, it is normally necessary to employ an equalizer at the receiver in order to correct the signal distortion introduced by the channel .ISI (inter symbol interference) leads to large error probability if it is not suppressed .The possible solutions for coping with ISI such as equalization technique. Maximum Likelihood Sequence Estimation (MLSE) implemented with Viterbi algorithm is the optimal equalizer for this ISI problem sense it minimizes the sequence of error rate. This estimator involves a very considerable amount of equipment complexity especially when detecting a multilevel digital signal having large alphabet, and/or operating under a channel with long impulse response, this arises a need to develop detection algorithms with reduced complexity without losing the performance. The aim of this work is to study the various ways to remove the ISI, concentrating on the decision-based algorithms (DFE, MLSE, and near MLSE), analyzing the difference between them from both performance and complexity point of view. An Improved non linear equalizer with Perturbation algorithm has been suggested which trying to enhance the performance and reduce the computational complexity by comparing it with the other existing detection algorithms., Comment: 11 pages, 8 figures

Nowadays, online Machine Translation (MT) is used widely with translation software, such as Google and Babylon, being easily available and downloadable. This study aims to test the translation quality of these two machine systems in translating Arabic news headlines into English. 40 Arabic news headlines were selected from three online sources, namely "Aljazeera," "daralhayat," and "Aawsat," where their English manually-translated versions were available. The selected data was evaluated by conducting criteria of Hutchins and Somers (1992) to find the assessment of each system outputs. Besides that, the selected data was also examined to find the types of translation techniques that are available in both machine outputs. A questionnaire was assigned to experienced professionals to evaluate the outputs to examine and determine which system was better to use in translating the collected data. The evaluation was based on criteria proposed by Hutchins and Somers. The findings indicated that both Google and Babylon had 80% of clarity, and Google scored a higher value of accuracy, i.e. 77.5%, compared to 75% of accuracy for Babylon. However, Babylon scored a higher value for style, i.e. 72.5%, compared to a score of 70% by Google. Nevertheless, the results revealed that online MT is undergoing improvement, and it has the potential to be one of the elements of globalization. As implication, the students could use online MT for learning purposes easily and quickly. (Contains 11 figures and 2 tables.)

Radiation induced cancer risks are driven by genetic instability. It is not well understood how different radiation sources induce genetic instability in cells with different genetic background. Here we report our studies on genetic instability, particularly chromosome instability using fluorescence in situ hybridization (FISH), in human primary lymphocytes, normal human fibroblasts, and transformed human mammary epithelial cells in a temporal manner after exposure to high energy protons and Fe ions. The chromosome spread was prepared 48 hours, 1 week, 2 week, and 1 month after radiation exposure. Chromosome aberrations were analyzed with whole chromosome specific probes (chr. 3 and chr. 6). After exposure to protons and Fe ions of similar cumulative energy (??), Fe ions induced more chromosomal aberrations at early time point (48 hours) in all three types of cells. Over time (after 1 month), more chromosome aberrations were observed in cells exposed to Fe ions than in the same type of cells exposed to protons. While the mammary epithelial cells have higher intrinsic genetic instability and higher rate of initial chromosome aberrations than the fibroblasts, the fibroblasts retained more chromosomal aberration after long term cell culture (1 month) in comparison to their initial frequency of chromosome aberration. In lymphocytes, the chromosome aberration frequency at 1 month after exposure to Fe ions was close to unexposed background, and the chromosome aberration frequency at 1 month after exposure to proton was much higher. In addition to human cells, mouse bone marrow cells isolated from strains CBA/CaH and C57BL/6 were irradiated with proton or Fe ions and were analyzed for chromosome aberration at different time points. Cells from CBA mice showed similar frequency of chromosome aberration at early and late time points, while cells from C57 mice showed very different chromosome aberration rate at early and late time points. Our results suggest that relative biological effectiveness (RBE) of radiation are different for different radiation sources, for different cell types, and for the same cell type with different genetic background at different times after radiation exposure. Caution must be taken in using RBE value to estimate biological effects from radiation exposure.

Exposure to radiation induces different types of DNA damage, increases mutation and chromosome aberration rates, and increases cellular transformation in vitro and in vivo. The susceptibility of cells to radiation depends on genetic background and growth condition of cells, as well as types of radiation. Mammalian cells of different tissue types and with different genetic background are known to have different survival rate and different mutation rate after cytogenetic insults. Genomic instability, induced by various genetic, metabolic, and environmental factors including radiation, is the driving force of tumorigenesis. Accurate measurements of the relative biological effectiveness (RBE) is important for estimating radiation-related risks. To further understand genomic instability induced by charged particles and their RBE, we exposed human lymphocytes ex vivo, human fibroblast AG1522, human mammary epithelial cells (CH184B5F5/M10), and bone marrow cells isolated from CBA/CaH(CBA) and C57BL/6 (C57) mice to high energy protons and Fe ions. Normal human fibroblasts AG1522 have apparently normal DNA damage response and repair mechanisms, while mammary epithelial cells (M10) are deficient in the repair of DNA DSBs. Mouse strain CBA is radio-sensitive while C57 is radio-resistant. Metaphase chromosomes at different cell divisions after radiation exposure were collected and chromosome aberrations were analyzed as RBE for different cell lines exposed to different radiations at various time points up to one month post irradiation.

An early stage of cancer development is believed to be genomic instability (GI) which accelerates the mutation rate in the descendants of the cells surviving radiation exposure. To investigate GI induced by charged particles, we exposed human lymphocytes, human fibroblast cells, and human mammary epithelial cells to high energy protons and Fe ions. In addition, we also investigated GI in bone marrow cells isolated from CBA/CaH (CBA) and C57BL/6 (C57) mice, by analyzing cell survival and chromosome aberrations in the cells after multiple cell divisions. Results analyzed so far from the experiments indicated different sensitivities to charged particles between CBA/CaH (CBA) and C57BL/6 (C57) mouse strains, suggesting that there are two main types of response to irradiation: 1) responses associated with survival of damaged cells and 2) responses associated with the induction of non-clonal chromosomal instability in the surviving progeny of stem cells. Previously, we reported that the RBE for initial chromosome damages was high in human lymphocytes exposed to Fe ions. Our results with different cell types demonstrated different RBE values between different cell types and between early and late chromosomal damages. This study also attempts to offer an explanation for the varying RBE values for different cancer types.

Numerous published studies have reported the Relative Biological Effectiveness (RBE) values for chromosome aberrations induced by charged particles of different LET. The RBE for chromosome aberrations in human lymphocytes exposed ex vivo has been suggested to show a similar relationship as the quality factor for cancer induction. Therefore, increased chromosome aberrations in the astronauts' white blood cells post long-duration missions are used to determine the biological doses from exposures to space radiation. However, the RBE value is known to be very different for different types of cancer. Previously, we reported that, even though the RBE for initial chromosome damages was high in human lymphocytes exposed to Fe ions, the RBE was significantly reduced after multiple cell divisions post irradiation. To test the hypothesis that RBE values for chromosome aberrations are cell type dependent, and different between early and late damages, we exposed human lymphocytes ex vivo, and human mammary epithelial cells in vitro to various charged particles. Chromosome aberrations were quantified using the samples collected at first mitosis post irradiation for initial damages, and the samples collected after multiple generations for the remaining or late arising aberrations. Results of the study suggested that the effectiveness of high-LET charged particles for late chromosome aberrations may be cell type dependent, even though the RBE values are similar for early damages.

Chromosomal translocations and inversions are considered stable, and cells containing these types of chromosome aberrations can survive multiple cell divisions. An efficient method to detect an inversion is multi-color banding fluorescent in situ hybridization (mBAND) which allows identification of both inter- and intrachromosome aberrations simultaneously. Post irradiation, chromosome aberrations may also arise after multiple cell divisions as a result of genomic instability. To investigate the stable or late-arising chromosome aberrations induced after radiation exposure, we exposed human lymphocytes to gamma rays and Fe ions ex vivo, and cultured the cells for multiple generations. Chromosome aberrations were analyzed in cells collected at first mitosis and at several time intervals during the culture period post irradiation. With gamma irradiation, about half of the damages observed at first mitosis remained after 7 day- and 14 day- culture, suggesting the transmissibility of damages to the surviving progeny. Detailed analysis of chromosome break ends participating in exchanges revealed a greater fraction of break ends involved in intrachromosome aberrations in the 7- and 14-day samples in comparison to the fraction at first mitosis. In particular, simple inversions were found at 7 and 14 days, but not at the first mitosis, suggesting that some of the aberrations might be formed days post irradiation. In contrast, at the doses that produced similar frequencies of gamma-induced chromosome aberrations as observed at first mitosis, a significantly lower yield of aberrations remained at the same population doublings after Fe ion exposure. At these equitoxic doses, more complex type aberrations were observed for Fe ions, indicating that Fe ion-induced initial chromosome damages are more severe and may lead to cell death. Comparison between low and high doses of Fe ion irradiation in the induction of late damages will also be discussed.

Chromosomal translocations and inversions are considered stable, and cells containing these types of chromosome aberrations can survive multiple cell divisions. An efficient method to detect an inversion is multi-color banding fluorescent in situ hybridization (mBAND) which allows identification of both inter- and intrachromosome aberrations simultaneously. Post irradiation, chromosome aberrations may also arise after multiple cell divisions as a result of genomic instability. To investigate the stable or late-arising chromosome aberrations induced after radiation exposure, we exposed human lymphocytes to gamma rays and Fe ions ex vivo, and cultured the cells for multiple generations. Chromosome aberrations were analyzed in cells collected at first mitosis and at several time intervals during the culture period post irradiation. With gamma irradiation, about half of the damages observed at first mitosis remained after 7 day- and 14 day- culture, suggesting the transmissibility of damages to the surviving progeny. Detailed analysis of chromosome break ends participating in exchanges revealed a greater fraction of break ends involved in intrachromosome aberrations in the 7- and 14-day samples in comparison to the fraction at first mitosis. In particular, simple inversions were found at 7 and 14 days, but not at the first mitosis, suggesting that some of the aberrations might be formed days post irradiation. In contrast, at the doses that produced similar frequencies of gamma-induced chromosome aberrations as observed at first mitosis, a significantly lower yield of aberrations remained at the same population doublings after Fe ion exposure. At these equitoxic doses, more complex type aberrations were observed for Fe ions, indicating that Fe ion-induced initial chromosome damages are more severe and may lead to cell death. Comparison between low and high doses of Fe ion irradiation in the induction of late damages will also be discussed.

Numerous published studies have reported the RBE values for chromosome chromosomes induced by charged particles of different LET. The RBE for chromosome aberrations in human lymphocytes exposed ex vivo showed a similar relationship as the quality factor for cancer induction. Consequently, increased chromosome aberrations in the astronauts' white blood cells post long-duration missions are used to determine the biological doses from exposures to space radiation. The RBE value is known to be very different for different types of cancer. Previously, we reported that the RBE for initial chromosome damages was high in human lymphocytes exposed to Fe ions. After multiple cell divisions post irradiation, the RBE was significantly smaller. To test the hypothesis that the RBE values for chromosome aberrations are different between early and late damages and also different between different cell types, we exposed human lymphocytes ex vivo, and human fibroblast cells and human mammary epithelial cells in vitro to 600 MeV/u Fe ions. Post irradiation, the cells were collected at first mitosis, or cultured for multiple generations for collections of remaining or late arising chromosome aberrations. The chromosome aberrations were quantified using fluorescent in situ hybridization (FISH) with whole chromosome specific probes. This study attempts to offer an explanation for the varying RBE values for different cancer types.

Stable type chromosome aberrations that survive multiple generations of cell division include translocation and inversions. An efficient method to detect an inversion is multi-color banding fluorescent in situ hybridization (mBAND) which allows identification of both inter- and intrachromosome aberrations simultaneously. Post irradiation, chromosome aberrations may also arise after multiple cell divisions as a result of genomic instability. To investigate the stable or late-arising chromosome aberrations induced after radiation exposure, we exposed human lymphocytes to gamma rays and Fe ions ex vivo, and cultured the cells for multiple generations. Chromosome aberrations were analyzed in cells collected at first mitosis and at several time intervals during the culture period post irradiation. With gamma irradiation, about half of the damages observed at first mitosis remained after 7 day- and 14 day- culture, suggesting the transmissibility of damages to the surviving progeny. At the doses that produced similar frequencies of gamma-induced chromosome aberrations as observed at first mitosis, a significantly lower yield of aberrations remained at the same population doublings after Fe ion exposure. At these equitoxic doses, more complex type aberrations were observed for Fe ions, indicating that Fe ion-induced initial chromosome damages are more severe and may lead to cell death. Detailed analysis of breaks participating in total chromosome exchanges within the first cell cycle post irradiation revealed a common hotspot located in the 3p21 region, which is a known fragile site corresponding to the band 6 in the mBand analysis. The breakpoint distribution in chromosomes collected at 7 days, but not at 14 days, post irradiation appeared similar to the distribution in cells collected within the first cell cycle post irradiation. The breakpoint distribution for human lymphocytes after radiation exposure was different from the previously published distribution for human mammary epithelial cells, indicating that interphase chromatin folding structures play a role in the distribution of radiation-induced breaks.

Understanding the radiation risks involved in spaceflight is of considerable importance, especially with the long-term occupation of ISS and the planned crewed exploration missions. Several independent causes may contribute to the overall risk to astronauts exposed to the complex space environment, such as exposure to GCR as well as SPES. Protons and high-Z energetic particles comprise the GCR spectrum and may exert considerable biological effects even at low fluence. There are also considerable uncertainties associated with secondary particle effects (e.g. HZE fragments, neutrons etc.). The interaction of protons and high-LET particles with biological materials at all levels of biological organization needs to be investigated fully in order to establish a scientific basis for risk assessment. The results of these types of investigation will foster the development of appropriately directed countermeasures. In this study, we compared the biological responses to proton irradiation presented to the target cells as a monoenergetic beam of particles of complex composition delivered to cells outside or inside a tissue phantom head placed in the United States EVA space suit helmet. Measurements of chromosome aberrations, apoptosis, and the induction of key proteins were made in bone marrow from CBA/CaJ and C57BL/6 mice at early and late times post exposure to radiation at 0, 0.5, 1 and 2 Gy while inside or outside of the helmet. The data showed that proton irradiation induced transmissible chromosomal/genomic instability in haematopoietic stem cells in both strains of mice under both irradiation conditions and especially at low doses. Although differences were noted between the mouse strains in the degree and kinetics of transforming growth factor-beta 1 and tumor necrosis factor-alpha secretion, there were no significant differences observed in the level of the induced instability under either radiation condition, or for both strains of mice. Consequently, when normalized to physical dose, the monoenergetic proton field present inside the helmet-protected phantom produced equivalent biological responses, when compared to unshielded cells, as measured by the induction of delayed genetic effects in murine haematopoietic stem cells.

Genomic instability is effectively induced by ionizing radiation. Recently, evidence has accumulated supporting a relationship between genetic background and the radiation-induced genomic instability phenotype. This is possibly due to alterations in proteins responsible for maintenance of genomic integrity or altered oxidative metabolism. Studies in human cell lines, human primary cells, and mouse models have been performed predominantly using high linear energy transfer (LET) radiation, or high doses of low LET radiation. The interplay between genetics, radiation response, and genomic instability has not been fully determined at low doses of low LET radiation. However, recent studies using low doses of low LET radiation suggest that the relationship between genetic background and radiation-induced genomic instability may be more complicated than these same relationships at high LET or high doses of low LET radiation. The complexity of this relationship at low doses of low LET radiation suggests that more of the population may be at risk than previously recognized and may have implications for radiation risk assessment.