Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence

The evolution of cybersecurity has spurred the emergence of autonomous threat hunting as a pivotal paradigm in the realm of AI-driven threat intelligence. This review navigates through the intricate landscape of autonomous threat hunting, exploring its significance and pivotal role in fortifying cyber defense mechanisms. Delving into the amalgamation of artificial intelligence (AI) and traditional threat intelligence methodologies, this paper delineates the necessity and evolution of autonomous approaches in combating contemporary cyber threats. Through a comprehensive exploration of foundational AI-driven threat intelligence, the review accentuates the transformative influence of AI and machine learning on conventional threat intelligence practices. It elucidates the conceptual framework underpinning autonomous threat hunting, spotlighting its components, and the seamless integration of AI algorithms within threat hunting processes.. Insightful discussions on challenges encompassing scalability, interpretability, and ethical considerations in AI-driven models enrich the discourse. Moreover, through illuminating case studies and evaluations, this paper showcases real-world implementations, underscoring success stories and lessons learned by organizations adopting AI-driven threat intelligence. In conclusion, this review consolidates key insights, emphasizing the substantial implications of autonomous threat hunting for the future of cybersecurity. It underscores the significance of continual research and collaborative efforts in harnessing the potential of AI-driven approaches to fortify cyber defenses against evolving threats

Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge

In this paper, we develop a generic methodology to encode hierarchical causality structure among observed variables into a neural network in order to improve its predictive performance. The proposed methodology, called causality-informed neural network (CINN), leverages three coherent steps to systematically map the structural causal knowledge into the layer-to-layer design of neural network while strictly preserving the orientation of every causal relationship. In the first step, CINN discovers causal relationships from observational data via directed acyclic graph (DAG) learning, where causal discovery is recast as a continuous optimization problem to avoid the combinatorial nature. In the second step, the discovered hierarchical causality structure among observed variables is systematically encoded into neural network through a dedicated architecture and customized loss function. By categorizing variables in the causal DAG as root, intermediate, and leaf nodes, the hierarchical causal DAG is translated into CINN with a one-to-one correspondence between nodes in the causal DAG and units in the CINN while maintaining the relative order among these nodes. Regarding the loss function, both intermediate and leaf nodes in the DAG graph are treated as target outputs during CINN training so as to drive co-learning of causal relationships among different types of nodes. As multiple loss components emerge in CINN, we leverage the projection of conflicting gradients to mitigate gradient interference among the multiple learning tasks. Computational experiments across a broad spectrum of UCI data sets demonstrate substantial advantages of CINN in predictive performance over other state-of-the-art methods. In addition, an ablation study underscores the value of integrating structural and quantitative causal knowledge in enhancing the neural network's predictive performance incrementally

Revisiting push-pull technology: Below and aboveground mechanisms for ecosystem services

Push-pull technology is an effective and ecological solution to suppressing major Lepidopteran pests of cereals, in particular stem-borers (Busseola fusca, Chilo partellus), the fall armyworm (Spodoptera frugiperda) and the parasitic weed Striga. The technology exploits plant-insect interactions in intercropping practices to manage the pests, increasing productivity while maintaining ecosystem resilience. In this study, we show that long-term (14 – 18 years) push-pull farming cause shifts in soil microbial communities, increasing the diversity of fungal taxa than bacteria. Further, the shift in the structure of soil microbial populations seems to require time to establish as observed by the weak impact of Desmodium species cultivated for just two years on soil microbial structure. However, even under the short-term cultivation period, individual taxa enrichment associated to the Desmodium were observed. On the other hand, whole soil microorganisms as well as rhizobia appeared to have little impact on the constitutive release of volatile emissions by Desmodium. Whether the plants grew on live or autoclaved soil, Desmodium did not release volatiles implicated in repelling lepidopteran pests, which is in stark contrast to previous reports. Upon herbivory of Desmodium by Spodoptera frugiperda larvae, a marginal increase in volatile emissions was observed. In line with this observation and contrary to expectation, intact Desmodium spp. did not deter oviposition by gravid S. frugiperda. In feeding bioassays, neonate S. frugiperda larvae strongly preferred Desmodium spp. to maize diet, but did not grow well nor did they survive on it. Older larvae were frequently immobilised on the stems, often dying in position after a few days. Deeper investigation showed that stems and leaves of Desmodium were covered with a dense web of straight and hooked silicon-rich trichomes of varying lengths that prevented larval movement, piercing their cuticle in the struggle. In this light, we propose that in a push-pull setting, Desmodium acts as a mechanical barrier and trap crop instead of a volatiles-dependent “push” crop as previously purported. In addition, intercropping practices have been shown to reduce insect pest populations through diverse mechanisms such as barrier effect and resource concentration. Push-pull technology shows that ecological approaches to pest management and increasing productivity can be effective. A clear understanding of the mechanisms of action of such approaches is critical for further improvements as well as translation into other agro-ecological practices

A mechanistic and a probabilistic model for predicting and analyzing microbiologically influenced corrosion

The complexities inherent in Microbiological Influenced Corrosion (MIC) requires a thorough understanding of the mechanisms involved when attempting to predict its rate. Even though mechanistic models have been developed in recent MIC studies, these models rarely analyze factors influencing pit depth and corrosion rate predicted. The objective of this work is to improve MIC prediction by quantitatively analyzing the factors influencing the predicted pit depth and corrosion rates. Therefore, this work presents a mechanistic and a probabilistic model which predicts corrosion rates, pit depth propagation, and analyzing influential factors in a MIC process. The mechanistic approach presents a model based on the direct contact extracellular electron transfer mechanism and nutrient limitation for microbial metabolism. The mechanistic model investigates the impact of redox intermediaries embedded in the cell structure of electroactive biofilms on corrosion rates. The mechanistic model also analyzes the effect of biofilm thickness limiting nutrient availability for corrosive microbiological organisms. The probabilistic approach presents a Bayesian network model which predicts the maximum corrosion rate in a process system. The probabilistic model analyzes the most critical factors affecting the corrosion rate predicted using Importance and Sensitivity analysis. The predictions obtained by both models were consistent with MIC rates in case studies and experimental studies. We also discovered that, redox properties of electroactive biofilms pose a significant threat to asset integrity as opposed to corrosion caused by sulfate reduction, in the case of Sulfate Reducing Bacteria (SRB)

Advanced immunotherapies for glioblastoma: tumor neoantigen vaccines in combination with immunomodulators

Correction to: Advanced immunotherapies for glioblastoma: tumor neoantigen vaccines in combination with immunomodulators. Acta Neuropathol Commun. 2023 Jul 12;11(1):116. doi: 10.1186/s40478-023-01600-2. PMID: 37438824.Glial-origin brain tumors, including glioblastomas (GBM), have one of the worst prognoses due to their rapid and fatal progression. From an oncological point of view, advances in complete surgical resection fail to eliminate the entire tumor and the remaining cells allow a rapid recurrence, which does not respond to traditional therapeutic treatments. Here, we have reviewed new immunotherapy strategies in association with the knowledge of the immune micro-environment. To understand the best lines for the future, we address the advances in the design of neoantigen vaccines and possible new immune modulators. Recently, the efficacy and availability of vaccine development with different formulations, especially liposome plus mRNA vaccines, has been observed. We believe that the application of new strategies used with mRNA vaccines in combination with personalized medicine (guided by different omic's strategies) could give good results in glioma therapy. In addition, a large part of the possible advances in new immunotherapy strategies focused on GBM may be key improving current therapies of immune checkpoint inhibitors (ICI), given the fact that this type of tumor has been highly refractory to ICI.This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project “CP21/00116 and PI22/0117” and co-funded by the European Union to RG, by “Asociación Española contra el Cancer (AECC) grant: INVES192GARG to RG and by Ministerio de Ciencia e Innovación and FEDER funds: PI21/01406 to JMSS.S

New Organizational Challenges in a Digital World: Securing Cloud Computing Usage and Reacting to Asset-Sharing Platform Disruptions

Information technology (IT) and IT-enabled business models are transforming the business ecosystem and posing new challenges for existing companies. This two-essay dissertation examines two such challenges: cloud security and the disruption of asset-sharing business models.The first essay examines how an organizations usage of cloud storage affects its likelihood of accidental breaches. The quasi-experiment in the U.S. healthcare sector reveals that organizations with higher levels of digitalization (i.e., Electronic Health Records levels) or those with more IT applications running on their internal data center are less likely to experience accidental breaches after using public cloud storage. We argue that digitalization and operational control over IT applications increase organizations awareness and capabilities of establishing a company-wide security culture, thereby reducing negligence related to physical devices and unintended disclosure after adopting cloud storage. The usage of cloud storage is more likely to cause accidental breaches for organizations contracting to more reputable or domain expert vendors. We explain this result as the consequence of less attention being focused on securing personally accessible data and physical devices given high reliance on reputed and knowledgeable cloud providers. This research is among the first to empirically examine the actual security impacts of organizations cloud storage usage and offers practical insights for cloud security management.The second essay examines how Asset-Sharing Business Model Prevalence (ASBMP) affects the performance implications of industry incumbent firms competitive actions when faced with entrants with asset-sharing business models, like Airbnb. ASBMP represents the amount of third-party products and services that originally were unavailable inside the traditional business model but now are orchestrated by asset-sharing companies in an industry. We use texting mining and econometrics approaches to analyze a longitudinal dataset in the accommodation industry. Our results demonstrate that incumbents competitive action repertoires (i.e., action volume, complexity, and heterogeneity) increase their performance when the ASBMP is high but decrease incumbents performance when the ASBMP is low. Practically, incumbents who are facing greater threat from asset-sharing firms can implement more aggressive competitive action repertoires and strategically focus on new product and M&A strategies. This research contributes to the literature of both competitive dynamics and asset-sharing business models

Development of the multi-scale dynamic modeling techniques for multi-modal brain recordings

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied in the micro- to meso-scale in studying the connections between the dynamical patterns and the behaviors, the investigation of the neural population dynamics is mainly limited by the single-scale analysis. My goal is to develop a multi-scale dynamical model for the collective activity of neuronal populations. First, I introduced a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales (Chapter 2). The NBGNet not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. I also show that the NBGNet robustly predicts held-out data across a long time scale (two weeks) without retraining. The effective connectivity defined from the presented model agrees with the established neuroanatomical hierarchy of motor control in the literature. I then introduced the m̲ulti-s̲cale n̲eural d̲ynamics n̲eural o̲rdinary d̲ifferential e̲quation (msDyNODE) to uncover multiscale brain communications governing cognitive behaviors (Chapter 3). I demonstrated that msDyNODE successfully captured multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived casual interactions between recording channels and scales not only aligned well with the abstraction of the hierarchical anatomy of the mammalian nervous system but also exhibited behavioral dependences. This work offers a new approach for in depth mechanistic studies on the brain where simultaneous acquisition of multi-scale neural activity is available. While the traditional neuronal tracing method serves as gold standard to assess the neural connectivity, toxicity and the limited efficiency prevent it from serving as a reliable tool to validate the multi-scale connectivity. It has been demonstrated that dendritic spines increase or become bigger after long-term potentiation while decrease or become smaller after long-term depression. Thus, we hypothesized the existence of excitatory and inhibitory connectivity can be revealed by the dynamics of dendritic spines. Since the dendritic spines can be smaller than the Abbe diffraction limit (~200 nm), we need an in-vivo super-resolution microscopy to achieve our goal. As a super-resolution imaging method, stimulated emission depletion (STED) microscopy has unraveled fine intracellular structures and provided insights into nanoscale organizations in cells. Although image resolution can be further enhanced by continuously increasing the STED-beam power, the resulting photodamage and phototoxicity are major issues for real-world applications of STED microscopy. In Chapter 4, I demonstrated that, with 50% less STED-beam power, the STED image resolution can be improved up to 1.45-fold using the separation of photons by a lifetime tuning (SPLIT) scheme combined with a deep learning-based phasor analysis algorithm termed flimGANE (f̲luorescence l̲ifetime i̲m̲aging based on a g̲enerative a̲dversarial n̲e̲twork). This work offers a new approach for STED imaging in situations where only a limited photon budget is available. In summary, I emphasized that this is the first time we are able to infer the nonlinear multiscale interactions in the brain networks by our multi-scale dynamic modeling approach. This opens a window of opportunity that multi-scale connectivity can provide a mechanistic understanding of brain computations underlying behaviors or mental states.Biomedical Engineerin

Next generation cereal crop yield enhancement: From knowledge of inflorescence development to practical engineering by genome editing

Artificial domestication and improvement of the majority of crops began approximately 10,000 years ago, in different parts of the world, to achieve high productivity, good quality, and widespread adaptability. It was initiated from a phenotype-based selection by local farmers and developed to current biotechnology-based breeding to feed over 7 billion people. For most cereal crops, yield relates to grain production, which could be enhanced by increasing grain number and weight. Grain number is typically determined during inflorescence development. Many mutants and genes for inflorescence development have already been characterized in cereal crops. Therefore, optimization of such genes could fine-tune yield-related traits, such as grain number. With the rapidly advancing genome-editing technologies and understanding of yield-related traits, knowledge-driven breeding by design is becoming a reality. This review introduces knowledge about inflorescence yield-related traits in cereal crops, focusing on rice, maize, and wheat. Next, emerging genome-editing technologies and recent studies that apply this technology to engineer crop yield improvement by targeting inflorescence development are reviewed. These approaches promise to usher in a new era of breeding practice

Computational and chemical approaches to drug repurposing

Drug repurposing, which entails discovering novel therapeutic applications for already existing drugs, provides numerous benefits compared to conventional drug discovery methods. This strategy can be pursued through two primary approaches: computational and chemical. Computational methods involve the utilization of data mining and bioinformatics techniques to identify potential drug candidates, while chemical approaches involve experimental screens oriented to finding new potential treatments based on existing drugs. Both computational and chemical methods have proven successful in uncovering novel therapeutic uses for established drugs. During my PhD, I participated in several experimental drug repurposing screens based on high-throughput phenotypic approaches. Finally, attracted by the potential of computational drug repurposing pipelines, I decided to contribute and generate a web platform focused on the use of transcriptional signatures to identify potential new treatments for human disease. A summary of these studies follows: In Study I, we utilized the tetracycline repressor (tetR)-regulated mechanism to create a human osteosarcoma cell line (U2OS) with the ability to express TAR DNA-binding protein 43 (TDP-43) upon induction. TDP-43 is a protein known for its association with several neurodegenerative diseases. We implemented a chemical screening with this system as part of our efforts to repurpose approved drugs. While the screening was unsuccessful to identify modulators of TDP-43 toxicity, it revealed compounds capable of inhibiting the doxycyclinedependent TDP-43 expression. Furthermore, a complementary CRISPR/Cas9 screening using the same cell system identified additional regulators of doxycycline-dependent TDP43 expression. This investigation identifies new chemical and genetic modulators of the tetR system and highlights potential limitations of using this system for chemical or genetic screenings in mammalian cells. In Study II, our objective was to reposition compounds that could potentially reduce the toxic effects of a fragment of the Huntingtin (HTT) protein containing a 94 amino acid long glutamine stretch (Htt-Q94), a feature of Huntington's disease (HD). To achieve this, we carried out a high-throughput chemical screening using a varied collection of 1,214 drugs, largely sourced from a drug repurposing library. Through our screening process, we singled out clofazimine, an FDA-approved anti-leprosy drug, as a potential therapeutic candidate. Its effectiveness was validated across several in vitro models as well as a zebrafish model of polyglutamine (polyQ) toxicity. Employing a combination of computational analysis of transcriptional signatures, molecular modeling, and biochemical assays, we deduced that clofazimine is an agonist for the peroxisome proliferator-activated receptor gamma (PPARγ), a receptor previously suggested to be a viable therapeutic target for HD due to its role in promoting mitochondrial biogenesis. Notably, clofazimine was successful in alleviating the mitochondrial dysfunction triggered by the expression of Htt-Q94. These findings lend substantial support to the potential of clofazimine as a viable candidate for drug repurposing in the treatment of polyQ diseases. In Study III, we explored the molecular mechanism of a previously identified repurposing example, the use of diethyldithiocarbamate-copper complex (CuET), a disulfiram metabolite, for cancer treatment. We found CuET effectively inhibits cancer cell growth by targeting the NPL4 adapter of the p97VCP segregase, leading to translational arrest and stress in tumor cells. CuET also activates ribosomal biogenesis and autophagy in cancer cells, and its cytotoxicity can be enhanced by inhibiting these pathways. Thus, CuET shows promise as a cancer treatment, especially in combination therapies. In Study IV, we capitalized on the Molecular Signatures Database (MSigDB), one of the largest signature repositories, and drug transcriptomic profiles from the Connectivity Map (CMap) to construct a comprehensive and interactive drug-repurposing database called the Drug Repurposing Encyclopedia (DRE). Housing over 39.7 million pre-computed drugsignature associations across 20 species, the DRE allows users to conduct real-time drugrepurposing analysis. This can involve comparing user-supplied gene signatures with existing ones in the DRE, carrying out drug-gene set enrichment analyses (drug-GSEA) using submitted drug transcriptomic profiles, or conducting similarity analyses across all database signatures using user-provided gene sets. Overall, the DRE is an exhaustive database aimed at promoting drug repurposing based on transcriptional signatures, offering deep-dive comparisons across molecular signatures and species. Drug repurposing presents a valuable strategy for discovering fresh therapeutic applications for existing drugs, offering numerous benefits compared to conventional drug discovery methods. The studies conducted in this thesis underscore the potential of drug repurposing and highlight the complementary roles of computational and chemical approaches. These studies enhance our understanding of the mechanistic properties of repurposed drugs, such as clofazimine and disulfiram, and reveal novel mechanisms for targeting specific disease pathways. Additionally, the development of the DRE platform provides a comprehensive tool to support researchers in conducting drug-repositioning analyses, further facilitating the advancement of drug repurposing studies

A systematic literature review on meta-heuristic based feature selection techniques for text classification

Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications