Approach Model Research Articles

Data-driven approach in a compartmental epidemic model to assess undocumented infections

Nowcasting and forecasting of epidemic spreading rely on incidence series of reported cases to derive the fundamental epidemiological parameters for a given pathogen. Two relevant drawbacks for predictions are the unknown fractions of undocumented cases and levels of nonpharmacological interventions, which span highly heterogeneously across different places and times. We describe a simple data-driven approach using a compartmental model including asymptomatic and pre-symptomatic contagions that allows to estimate both the level of undocumented infections and the value of effective reproductive number Rt from time series of reported cases, deaths, and epidemiological parameters. The method was applied to epidemic series for COVID-19 across different municipalities in Brazil allowing to estimate the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states. The methods described here can be extended to more general cases if data is available and adapted to other epidemiological approaches and surveillance data.

Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications

Recent advancements and developments in artificial intelligence (AI) based approaches have shifted the manufacturing practices towards the fourth industrial revolution, considered as Industry 4.0 practices. A positive impact of AI-based techniques on sustainability can be seen in manufacturing organisations’ at the system, product and process levels. Adopting AI-based strategies in manufacturing improves decision making, productivity and system performance. Despite sustainability and other benefits, the adoption of AI-based approaches in manufacturing organisations is still limited due to employees’ knowledge and digital skills. In the present time, due to the digitalisation of manufacturing activities, intelligent sensors, and supply chain activities, industries are facing challenges with the generation of high volume, different variety and velocity of data. This data can be helpful for manufacturing organisations to enhance their performance and sustainability. However, managing this big data is still a significant challenge due to a lack of knowledge and limited literature. Deep learning (DL) based models can be a suitable choice to provide advanced analytics tools for manufacturing data processing and analysis. However, literature on the DL is still limited in the manufacturing context with its relationship to sustainability. The present study discusses the evolution of DL approaches in manufacturing and different DL-based models. This study also highlights how DL-based approaches can enhance the sustainability performance of industries. In the study, primary research areas, i.e., fault diagnosis, quality management, and predictive maintenance, have been discussed. Finally, a conceptual DL-based framework is proposed for the manufacturing industries to enhance their sustainability performance in manufacturing activities.

Protein Kinase CK2 and Its Potential Role as a Therapeutic Target in Huntington's Disease.

Huntington’s Disease (HD) is a devastating neurodegenerative disorder caused by a CAG trinucleotide repeat expansion in the HTT gene, for which no disease modifying therapies are currently available. Much of the recent research has focused on developing therapies to directly lower HTT expression, and while promising, these therapies have presented several challenges regarding administration and efficacy. Another promising therapeutic approach is the modulation of HTT post-translational modifications (PTMs) that are dysregulated in disease and have shown to play a key role in HTT toxicity. Among all PTMs, modulation of HTT phosphorylation has been proposed as an attractive therapeutic option due to the possibility of orally administering specific kinase effectors. One of the kinases described to participate in HTT phosphorylation is Protein Kinase CK2. CK2 has recently emerged as a target for the treatment of several neurological and psychiatric disorders, although its role in HD remains controversial. While pharmacological studies in vitro inhibiting CK2 resulted in reduced HTT phosphorylation and increased toxicity, genetic approaches in mouse models of HD have provided beneficial effects. In this review we discuss potential therapeutic approaches related to the manipulation of HTT-PTMs with special emphasis on the role of CK2 as a therapeutic target in HD.

Clinical and Molecular Implications of Osteopontin in Heart Failure.

The matricellular protein osteopontin modulates cell–matrix interactions during tissue injury and healing. A complex multidomain structure of osteopontin enables it not only to bind diverse cell receptors but also to interact with various partners, including other extracellular matrix proteins, cytokines, and growth factors. Numerous studies have implicated osteopontin in the development and progression of myocardial remodeling in diverse cardiac diseases. Osteopontin influences myocardial remodeling by regulating extracellular matrix production, the activity of matrix metalloproteinases and various growth factors, inflammatory cell recruitment, myofibroblast differentiation, cardiomyocyte apoptosis, and myocardial vascularization. The exploitation of osteopontin loss- and gain-of-function approaches in rodent models provided an opportunity for assessment of the cell- and disease-specific contribution of osteopontin to myocardial remodeling. In this review, we summarize the recent knowledge on osteopontin regulation and its impact on various cardiac diseases, as well as delineate complex disease- and cell-specific roles of osteopontin in cardiac pathologies. We also discuss the current progress of therapeutics targeting osteopontin that may facilitate the development of a novel strategy for heart failure treatment.

Analysis of control strategies for VIVA OpenHBM with active reflexive neck muscles

Modeling muscle activity in the neck muscles of a finite element (FE) human body model can be based on two biological reflex systems. One approach is to approximate the Vestibulocollic reflex (VCR) function, which maintains the head orientation relative to a fixed reference in space. The second system tries to maintain the head posture relative to the torso, similar to the Cervicocolic reflex (CCR). Strategies to combine these two neck muscle controller approaches in a single head-neck FE model were tested, optimized, and compared to rear-impact volunteer data. The first approach, Combined-Control, assumed that both controllers simultaneously controlled all neck muscle activations. In the second approach, Distributed-Control, one controller was used to regulate activation of the superficial muscles while a different controller acted on deep neck muscles. The results showed that any muscle controller that combined the two approaches was less effective than only using one of VCR- or CCR-based systems on its own. A passive model had the best objective rating for cervical spine kinematics, but the addition of a single active controller provided the best response for both head and cervical spine kinematics. The present study demonstrates the difficulty in completely capturing representative head and cervical spine responses to rear-impact loading and identified a controller capturing the VCR reflex as the best candidate to investigate whiplash injury mechanisms through FE modeling.

Numerical and experimental studies of microcapsules phase change material in a pulsating fluidized bed as an energy storage medium

Phase change materials (PCMs) and particularly microencapsulated phase change materials (MPCM) have introduced new ways to enhance the energy storage capacity of a system due to the high latent heat and high storage density of these materials. This experimental and numerical research aims to investigate the process in a pulsating fluidized bed with MPCMs operating as an energy storage device. Experiments were carried out with MPCM (PX52) in a pulsating fluidized bed with the 0.2 m ID and 1 m height. Numerical simulation was performed using the Eulerian-Lagrangian approach in a symmetric two-dimensional model. Pulsating gas flow at a temperature of 60 °C with a frequency range of 1–10Hz for 200 μm mean diameter was used in the experiment and simulation. The effect of pulsating on the efficiency of the energy storage was examined at 1.5, 2, and 2.5 of the minimum fluidization velocity (Umf). Also, simulation and experiment were performed in the continuous mode for comparison with the published research of others. Results of simulation and experimental data of this study and others were noticed in close agreement. The simulation and experimental results revealed an optimal pulsating frequency, at which the energy storage in the fluidized bed is highest for 2 Umf in 7Hz, which is 92% for pulsating and 64% for continuous flow. Besides, using 7Hz and 10Hz frequencies enhanced the charging time with higher efficiency and reached the stabilized temperature in a shorter time.

Comprehensive and systematic characterization of multi-functionalized cisplatin nano-conjugate: from the chemistry and proteomic biocompatibility to the animal model

BackgroundNowadays, nanoparticles (NPs) have evolved as multifunctional systems combining different custom anchorages which opens a wide range of applications in biomedical research. Thus, their pharmacological involvements require more comprehensive analysis and novel nanodrugs should be characterized by both chemically and biological point of view. Within the wide variety of biocompatible nanosystems, iron oxide nanoparticles (IONPs) present mostly of the required features which make them suitable for multifunctional NPs with many biopharmaceutical applications.ResultsCisplatin-IONPs and different functionalization stages have been broadly evaluated. The potential application of these nanodrugs in onco-therapies has been assessed by studying in vitro biocompatibility (interactions with environment) by proteomics characterization the determination of protein corona in different proximal fluids (human plasma, rabbit plasma and fetal bovine serum),. Moreover, protein labeling and LC–MS/MS analysis provided more than 4000 proteins de novo synthetized as consequence of the nanodrugs presence defending cell signaling in different tumor cell types (data available via ProteomeXchanges with identified PXD026615). Further in vivo studies have provided a more integrative view of the biopharmaceutical perspectives of IONPs.ConclusionsPharmacological proteomic profile different behavior between species and different affinity of protein coating layers (soft and hard corona). Also, intracellular signaling exposed differences between tumor cell lines studied. First approaches in animal model reveal the potential of theses NPs as drug delivery vehicles and confirm cisplatin compounds as strengthened antitumoral agents.

Electromagnetic transition amplitude for Roper resonance from holographic QCD

The Roper resonance, the first excited state of the nucleon, Is one of the best-established baryon resonances. Yet, its properties have not been consistently explained by effective models of QCD, such as the non-relativistic quark model. In this letter, we propose an alternative approach in the Sakai-Sugimoto model that is one of the holographic models of QCD. In particular, we analyze the helicity amplitude of the electromagnetic transitions at the leading of 't~Hooft coupling $1/\lambda$. The model incorporates baryon structure at short distance by non-linear mesons surrounded by meson clouds at long distance. We demonstrate that the recently observed data by CLAS are explained in the present approach.

Automatic model calibration of combined hydrologic, hydraulic and stormwater quality models using approximate Bayesian computation

A range of automatic model calibration techniques are used in water engineering practice. However, use of these techniques can be problematic due to the requirement of evaluating the likelihood function. This paper presents an innovative approach for overcoming this issue using a calibration framework developed based on Approximate Bayesian Computation (ABC) technique. Use of ABC in automatic model calibration was undertaken for a combined urban hydrologic, hydraulic and stormwater quality model. The simulated runoff hydrograph and total suspended solid (TSS) pollutograph were compared with observed data for multiple events from three different catchments, and found to be within 95% confidence intervals of the simulated results. The R programmed model was validated by comparing simulated flow with similar commercially available modeling software, MIKE URBAN output determined using mean value of parameters obtained from the calibration exercise, and performed well by satisfying statistical criteria's such as coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME). The developed framework is useful for automatic calibration and uncertainty estimation using ABC approach in complex hydrologic, hydraulic and stormwater quality models with multi-input-output systems.

Beyond cost reduction: improving the value of energy storage in electricity systems

From a macro-energy system perspective, an energy storage is valuable if it contributes to meeting system objectives, including increasing economic value, reliability and sustainability. In most energy systems models, reliability and sustainability are forced by constraints, and if energy demand is exogenous, this leaves cost as the main metric for economic value. Traditional ways to improve storage technologies are to reduce their costs; however, the cheapest energy storage is not always the most valuable in energy systems. Modern techno-economical evaluation methods try to address the cost and value situation but do not judge the competitiveness of multiple technologies simultaneously. This paper introduces the ‘market potential method’ as a new complementary valuation method guiding innovation of multiple energy storage. The market potential method derives the value of technologies by examining common deployment signals from energy system model outputs in a structured way. We apply and compare this method to cost evaluation approaches in a renewables-based European power system model, covering diverse energy storage technologies. We find that characteristics of high-cost hydrogen storage can be more valuable than low-cost hydrogen storage. Additionally, we show that modifying the freedom of storage sizing and component interactions can make the energy system 10% cheaper and impact the value of technologies. The results suggest looking beyond the pure cost reduction paradigm and focus on developing technologies with suitable value approaches that can lead to cheaper electricity systems in future.Graphical

Quantum Rényi entropy by optimal thermodynamic integration paths

Despite being a well-established operational approach to quantify entanglement, Rényi entropy calculations have been plagued by their computational complexity. We introduce here a theoretical framework based on an optimal thermodynamic integration scheme, where the Rényi entropy can be efficiently evaluated using regularizing paths. This approach avoids slowly convergent fluctuating contributions and leads to low-variance estimates. In this way, large system sizes and high levels of entanglement in model or first-principles Hamiltonians are within our reach. We demonstrate this approach in the one-dimensional quantum Ising model and perform an evaluation of entanglement entropy in the formic acid dimer, by discovering that its two shared protons are entangled even above room temperature.Received 29 December 2021Accepted 9 June 2022DOI:https://doi.org/10.1103/PhysRevResearch.4.L032002Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasChemical Physics & Physical ChemistryEntanglement entropyPath integralsQuantum phase transitionsTechniquesMonte Carlo methodsCondensed Matter, Materials & Applied PhysicsQuantum InformationStatistical Physics

From customer readiness to customer retention: the mediating role of customer psychological and behavioral engagement

PurposeCustomer engagement (CE) literature features divergent definitions and conceptualizations. To clarify its meaning, antecedents and outcomes, this paper aims to propose that psychological customer engagement (PCE) is the mechanism by which customers’ readiness to engage influences behavioral customer engagement (BCE) in the form of in-role and extra-role behaviors, which then affect customers’ goal attainment, satisfaction and retention.Design/methodology/approachSet in the fitness center industry, this study combines perceptual data (from customers) and behavioral data (from the fitness center) to reveal a hierarchy of effects: customer readiness to PCE to BCE to customer goal attainment, satisfaction and retention.FindingsCustomer readiness variables (role clarity, ability, motivation) influence in-role and extra-role BCE directly and indirectly through PCE. Extra-role BCE is associated with goal attainment and satisfaction, and the latter is linked to customer retention. In-role BCE is associated with goal attainment only.Research limitations/implicationsThe proposed integrative model bridges the psychological–behavioral divide in CE literature and encourages the adoption of a broader nomological network that accounts for the effects of customers’ characteristics and actions on their goal attainment, satisfaction and retention.Practical implicationsManagers can enhance CE by improving customer role clarity, ability and motivation. Relative to in-role BCE, extra-role BCE appears more critical because it affects both goal attainment and satisfaction directly and retention indirectly.Originality/valueThe novel integrative approach, combining BCE and PCE in a single model, also provides a consumer-oriented view on CE, which establishes a more comprehensive perspective, as summarized in the proposed model of consumer engagement.

Could trees change future behaviour in xylogenesis to improve fitness based on past and current conditions? A tropical case of study

Carbon allocation is the Achilles' heel of forest models, due to the difficulty to predict responses induced by environmental changes. Structural-functional models involve the plant's strategy to split its resources between organs and functions. An important functional characteristic is xylem architecture, which is connected to water use strategy and plant productivity besides one of the most important C-sink (that absorbs carbon). Our goal is to use the information present in the tree rings of Schizolobium parahyba, to expand the possibilities of teleonomic approaches in structural-functional models of tree species. In a comparative xylem rings analyses, we established relationships among specific gravity, vessel traits as hydraulic conductivity potential, double wall thickness, and autofluorescence intensity of lignin. The analyses showed that growth ring characteristics seem to have been modulated not only by water availability of the period of wood formation but also by the water availability of previous years. This relationship would be occurring due to the greater capacity of accumulation of reserve carbohydrates in years whose climatic conditions were favourable. This pattern of behaviour entails the formation of two distinct growth ring patterns, one with high and the other with low structural cost. This case study shows that the species can change future behaviour in xylogenesis to improve fitness based on past and current conditions. Our study may help to extend the possibilities of teleonomic approaches in structural-functional models of tree species, which will assist in a greater understanding of how trees balance their carbon allocation in wood according to changes in the environment.

Development of consistent fish-bone simplified model with energy-based approach for efficient seismic evaluation of irregular steel moment resisting frames

This study aims to develop the Consistent Fish-Bone (CFB) simplified model for efficient estimation of Engineering Demand Parameters (EDPs) in irregular steel Moment Resisting Frames (steel-MRFs). To achieve this goal, some modifications based on the energy consistent approach have been applied to the Improved Fish-Bone (IFB) simplified model previously presented for reinforced concrete MRFs. These modifications include: 1) adding truss elements to the IFB model to consider the effect of flexural deformations and determining their areas by balancing the overturning moment and the strain energy due to the axial deformation of columns in the original steel-MRF with the overturning moment and the strain energy of the truss elements in the simplified model and 2) determination of the elastic properties of beams and columns in the simplified model based on the compatibility of the energy absorbed by these elements and the corresponding elements in the original steel-MRF. By applying the mentioned modifications, the proposed CFB model is obtained, which can be used in flexible steel buildings without the limitations of the IFB model. In addition, the proposed CFB model is geometrically similar to the Modified Fish-Bone (MFB) simplified model previously presented for steel-MRF buildings with equal bay lengths. However, the energy-based approach in the CFB model leads to its use in buildings with unequal bay lengths. In order to investigate the accuracy of the proposed simplified model, three 3-, 9-, and 20-story steel-MRFs with irregular bay lengths and three other steel-MRFs with the same number of stories but with vertical geometric irregularity have been used. Then, the efficiency of the proposed simplified model in estimating the static and dynamic behavior of the structure is evaluated using nonlinear static analysis (pushover), Nonlinear Time History Analysis (NLTHA), Incremental Dynamic Analysis (IDA), and seismic fragility analysis. In the seismic evaluation process, 44 far-field and 28 near-field ground motions of FEMA P-695 at different seismic hazard levels are used. The results indicate that the proposed simplified model has high efficiency in low-to high-rise irregular steel-MRFs at all seismic hazard levels up to the collapse stage by properly estimating the EDPs and significantly reducing the Degrees of Freedom (DOFs) of the original steel-MRF. Therefore, this model can be very useful in reducing the computational costs and complexities of the new generation of Performance-Based Earthquake Engineering (PBEE).

The Productivity Effects of Adopting Improved Organic Manure Practices in Nepal

Owing to farmers’ issues on accessibility and affordability of chemical fertilizers in Nepal, organic manure has been advocated as a more environmentally sustainable fertilizer input alternative. Using household survey data from Nepal, we analyze the adoption and productivity effects of improved organic manure practices. Our empirical strategy utilizes a recently developed control function approach in the endogenous switching regression model that allows for substantial heterogeneities accounting for potential self-selection problem due to observables and unobservables. Findings reinforce the crucial role of social capital as farm households’ adoption decisions are significantly influenced by 1) having adopter neighbors or friends, 2) residing in proximity to the market, and 3) gaining easy access to agricultural extension services. Beyond environmental and social considerations, this study establishes economic motivations for the adoption decision. The results show that the adoption of improved organic manure practices increased crop yield by 17 percentage points. These practices did not only result in significantly higher yields among adopting farms, but counterfactual analyses indicate that non-adopting farms would have realized much better yield conditions had they implemented the technology. The study concludes with implications for policies and initiatives to expand the adoption and impacts of improved organic manure practices in Nepal.

Asset Profitability in the Electricity Sector: An Iterative Approach in a Linear Optimization Model

In a competitive electricity market, generation capacities can exactly cover their full costs. However, the real market deviates from this ideal in some aspects. One is the concern of non-existent or insufficient scarcity prices. We present an iterative method in a linear optimization model to investigate the profitability of assets in the absence of scarcity prices and how the system changes when this risk is incorporated into investors’ expectations. Therefore, we use a two-step optimization of capacity planning and unit commitment. Iteratively, mark-ups at the height of uncovered costs are added to investment costs. This typically leads to a system with better investment profitability while keeping the system cost increase low. The methodology is applied to a simplified brownfield generation system, targeting CO2-free power generation within 25 years. In a model with annual foresight of actors, iterations result in a generation system with significantly lower (or even no) uncovered costs for new investments within ten or fewer iterations. Our example case with full foresight shows that early-added gas (combined cycle) and wind onshore capacities are able to recover their full costs over a lifetime, even without scarcity prices. However, the contribution margin gap remains high, especially for storage and biomass.

Abstract 626: Personalized immunotherapeutic platform with evidence of clinical activity in glioblastoma (IGV-001) protects mice against other lethal solid tumor challenges

Abstract Background: IGV-001 is a novel immunotherapy that combines irradiated patient-derived glioblastoma tumor cells and an antisense oligonucleotide against insulin-like growth factor type 1 receptor (IMV-001) in biodiffusion chambers (BDC). We recently reported that IGV-001-treated newly diagnosed glioblastoma patients with methylated O6-methylguanine-DNA methyl-transferase promoter had a median progression free survival of 38.4 months compared with 8.3 months in historical standard-of-care-treated patients (P=0.0008). We have now found activity with the equivalent murine approach in multiple cancer models, highlighting the transformative potential of this immunotherapeutic platform beyond glioblastoma. Methodology: We utilized several murine models including the ID8-luciferase (-Luc) intraperitoneal ovarian cancer and Hepa1-6-Luc hepatocellular carcinoma orthotopic model. BDC containing saline or 1x106 IMV-001-treated tumor cells (hereon IOV-001 and IHV-001, respectively) were implanted in flanks of C57BL/6 mice and explanted 48 h later, as per glioblastoma clinical protocol. Tumor challenge for all models was conducted 28d after chamber implantation. Mice were monitored for survival and tumor growth, as determined by bioluminescence intensity. Cytokine assays and immunophenotyping were conducted. Results: 60% of IOV-001-treated mice were alive 58d post-tumor challenge, compared to only 19% of mice in the saline group (MST=37d, p=0.004). In the Hepa1-6 model, 50% of IHV-001-treated mice were alive by 100d post-tumor challenge (MST = 60.5d). In comparison, there were no survivors in the saline group beyond 28d (MST = 18d; p = 0.004). Most of the long-term survivors had undetectable tumor; a fraction showed some level of tumor burden which rose and fell, demonstrating immune control. Circulating IFNγ was significantly higher in IOV-001-treated mice compared to controls on 1d post-tumor challenge (p<0.001) and trended higher in those receiving IHV-001 14d after tumor challenge. Positive correlations were found between percentages of CD8+PD1+and CD4+TIM3+, and tumor burden in IOV-001-treated mice. On 14d, PD1+ expression in both CD4+ and CD8+ T cell subsets was significantly lower in surviving mice treated with IHV-001. Improved survival using this approach was also observed in the Pan02 orthotopic pancreatic model. Latest data will be presented. Conclusions: These data support the antitumor activity of this novel immunotherapeutic platform in multiple cancers beyond glioblastoma. Results suggest that efficacy is associated with a systemic immunological response, resulting in generation of Th1 antitumor cytotoxic T cells. Future studies are seeking to resolve the factors triggering good v. poor responses using phenotypic evaluation of T cell activation/exhaustion markers and Th1/Th2 cytokine production. Citation Format: Jenny Zilberberg, Amelia Zellander, Christopher Uhl, Christopher Cultrara, Kenneth kirby, Essam Elazaq, Charles B. Scott, David W. Andrews, Mark Exley. Personalized immunotherapeutic platform with evidence of clinical activity in glioblastoma (IGV-001) protects mice against other lethal solid tumor challenges [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 626.

Delivery of an ectonucleotidase inhibitor with ROS-responsive nanoparticles overcomes adenosine-mediated cancer immunosuppression.

Tumor evasion of immune destruction is associated with the production of immunosuppressive adenosine in the tumor microenvironment (TME). Anticancer therapies can trigger adenosine triphosphate (ATP) release from tumor cells, causing rapid formation of adenosine by the ectonucleotidases CD39 and CD73, thereafter exacerbating immunosuppression in the TME. The goal of this study was to develop an approach to facilitate cancer therapy-induced immunogenic cell death including ATP release and to limit ATP degradation into adenosine, in order to achieve durable antitumor immune response. Our approach was to construct reactive oxygen species (ROS)-producing nanoparticles that carry an ectonucleotidase inhibitor ARL67156 by electronic interaction and phenylboronic ester. Upon near-infrared irradiation, nanoparticle-produced ROS induced ATP release from MOC1 cancer cells in vitro and triggered the cleavage of phenylboronic ester, facilitating the release of ARL67156 from the nanoparticles. ARL67156 prevented conversion of ATP to adenosine and enhanced anticancer immunity in an MOC1-based coculture model. We tested this approach in mouse tumor models. Nanoparticle-based ROS-responsive drug delivery reprogramed the immunogenic landscape in tumors, eliciting tumor-specific T cell responses and tumor regression, conferring long-term survival in mouse models. We demonstrated that TME reprograming sets the stage for response to anti-programmed cell death protein 1 (PD1) immunotherapy, and the combination resulted in tumor regression in a 4T1 breast cancer mouse model that was resistant to PD1 blockade. Furthermore, our approach also induced immunological effects in patient-derived organotypic tumor spheroid model, suggesting potential translation of our nanoparticle approach for treating human cancers.

Parameter Estimation of GARCH Model with Bootstrap Approximation

This study aims to estimate the parameters of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model using a bootstrap approach. In the heteroscedasticity data model, it is determined how much the residual value of the sample used is. The bootstrap approach is a non-parametric and resampling technique used to estimate the parameter. From the sample data implemented, the residual estimation using the Maximum Likelihood Estimation method is - 0.065851304. Furthermore, the residual estimation value using the bootstrap approach is -1.769129241. Thus, the use of the bootstrap approach in the GARCH model results in a smaller residual value than MLE.

Gasdermin B over-expression modulates HER2-targeted therapy resistance through LC3B/Rab7 interaction

Tumor resistance to therapy is one of the most important challenges in current oncology. A clear example of this major issue is the HER2-positive tumors characterized by ERBB2 (HER2/neu) overexpression. HER2+ overexpression/ amplification occurs in several tumors, especially in breast and gastric carcinomas that show an aggressive phenotype traditionally associated to poor prognosis. In fact, despite the clinical outcome of these tumors has gradually improved, due to the development of different antiHER2 therapies, many patients will not respond to these treatments or develop acquired resistance. Several potential resistance mechanisms to these targeted therapies have been described. Among them, our laboratory established, for the first time, that the amplification/overexpression of Gasdermin B (GSDMB), which occurs in more than 60% HER2+ breast cancer patients, is a marker of poor prognosis. Moreover, our data in HER2+ tumors revealed that GSDMB overexpression correlated to distant metastasis, poor clinical outcome, and reduced response to anti-HER2 therapies. In this context, we have attempted to elucidate the molecular mechanism whereby GSDMB collaborates in the resistance to anti-HER2 therapies. To decipher the functional relevance of GSDMB in promoting resistance to HER2-targeted therapies we performed several molecular approaches (immunoblot, flow cytometry, immunoprecipitation, etc.) in different breast and gastric carcinoma cell models. Finally, we validated the efficacy of the identified targeted treatment using two complementary in vivo preclinical models (mice and zebrafish). Our results confirmed that similarly to HER2 breast tumors, GSDMB is also over-expressing in more than 50% in HER2 gastric tumors. Furthermore, this over-expression renders HER2 breast and gastric cancer cells more resistant to anti- HER2 agents by promoting protective autophagy. Consistent with this, we proved that the combination of lapatinib with the autophagy inhibitor chloroquine increases the therapeutic response specifically in GSDMB-positive tumors in vitro and in vivo. Mechanistically, we also confirmed that GSDMB forms a complex with LC3B and Rab7. Finally, we validated these results in clinical samples of HER2 breast and gastric cancers, where GSDMB/LC3B/Rab7 co- expression associates significantly with relapse. In conclusion, our data decipher the molecular mechanism that underlies the relation between Gasdermin B and pro-survival autophagy with the final goal of finding a therapy that would be effective in overcoming resistance to anti-HER2 standard therapies in HER2/GSDMB+ tumors.