A review on comprehending immunotherapeutic approaches inducing ferroptosis: Managing tumour immunity.

Ferroptosis, a necrotic, iron-dependent controlled cell death mechanism, is distinguished by the development of lipid peroxides to fatal proportions. Malignant tumours, influenced by iron to promote fast development, are vulnerable to ferroptosis. Based upon mounting evidence it has been observed that ferroptosis may be immunogenic and hence may complement immunotherapies. A new approach includes iron oxide-loaded nano-vaccines (IONVs), having supremacy for the traits of the tumour microenvironment (TME) to deliver specific antigens through improving the immunostimulatory capacity by molecular disintegration and reversible covalent bonds that target the tumour cells and induce ferroptosis. Apart from IONVs, another newer approach to induce ferroptosis in tumour cells is through oncolytic virus (OVs). One such oncolytic virus is the Newcastle Disease Virus (NDV), which can only multiply in cancer cells through the p53-SLC7A11-GPX4 pathway that leads to elevated levels of lipid peroxide and intracellular reactive oxygen species leading to the induction of ferroptosis that induce ferritinophagy.

Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI.

The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.

Human Mycobiota and Its Role in Cancer Progression, Diagnostics and Therapeutics: A Link Lesser-Known.

Although not as well studied as the bacterial component of the human microbiota, the commensal fungi or mycobiota play important roles in maintaining our health by augmenting our immune system. This mycobiota is also associated with various fatal diseases like opportunistic mycoses, and even cancer, with different cancers having respective type-specific mycobiota. The different fungal species which comprise these different intratumoral mycobiota play important roles in cancer progression. The aim of this review paper is to decipher the association between mycobiota and cancer, and shed light on new avenues in cancer diagnosis, and the development of new anti-cancer therapeutics.

Visible light-mediated synthesis of quinazolinones and benzothiadiazine-1,1-dioxides utilizing aliphatic alcohols.

The activation and utilization of challenging aliphatic alcohols like methanol and ethanol is a very appealing approach to synthesize valuable organic molecules. Utilization of methanol and ethanol as a coupling partner has emerged as a valuable alternative to synthesize industrially relevant N-heterocycles because they can be easily procured from renewable sources unlike other activated coupling partners which are expensive and also unstable. Herein, a mild and metal-free photocatalytic protocol to synthesize quinazolinones and more challenging benzothiadiazine-1,1-dioxides, which is unprecedented at room temperature, is demonstrated. This methodology showcased broad substrate scope and provided important N-heterocycles more efficiently than the transition metal-based high temperature protocols. An unexplored reactivity with allyl alcohol is observed following the developed protocol. A series of control experiments were carried out to understand the mechanism.

A robust optimal control framework for controlling aberrant RTK signaling pathways in esophageal cancer.

This study presents a new framework for obtaining personalized optimal treatment strategies targeting aberrant signaling pathways in esophageal cancer, such as the epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) signaling pathways. A new pharmacokinetic model is developed taking into account specific heterogeneities of these signaling mechanisms. The optimal therapies are designed to be obtained using a three step process. First, a finite-dimensional constrained optimization problem is solved to obtain the parameters of the pharmacokinetic model, using discrete patient data measurements. Next, a sensitivity analysis is carried out to determine which of the parameters are sensitive to the evolution of the variants of EGF receptors and VEGF receptors. Finally, a second optimal control problem is solved based on the sensitivity analysis results, using a modified pharmacokinetic model that incorporates two representative drugs Trastuzumab and Bevacizumab, targeting EGF and VEGF, respectively. Numerical results with the combination of the two drugs demonstrate the efficiency of the proposed framework.

Antiplasmodial action of 4-nitrobenzenesulfonamide chalcones: Design, synthesis, characterisation, in vitro and in silico evaluation against blood stages of Plasmodium falciparum 3D7.

Malaria is an intracellular protozoan parasitic disease caused by Plasmodium species with significant morbidity and mortality in endemic regions. The complex lifecycle of the parasite and the emergence of drug-resistant Plasmodium falciparum have hampered the efficacy of current anti-malarial agents. To circumvent this situation, the present study attempts to demonstrate the blood-stage anti-plasmodial action of 26 hybrid compounds containing the three privileged bioactive scaffolds (sulfonamide, chalcone, and nitro group) with synergistic and multitarget action. These three parent scaffolds exhibit divergent activities, such as antibacterial, anti-malarial, anti-fungal, anti-inflammatory, and anticancer. All the synthesised compounds were characterised using various spectroscopic techniques. The in vitro blood-stage inhibitory activity of 26 hybrid compounds was evaluated against mixed-stage culture (asynchronize) of human malarial parasite P. falciparum, Pf 3D7 at different concentrations ranging from 25.0 µg/mL to 0.78 µg/mL using SYBR 1 green assay, with IC50 values determined after 48 h of treatment based on the drug-response curves. Two potent compounds (11 and 10), with 2-Br and 2,6-diCl substitutions, showed pronounced activity with IC50 values of 5.4 µg/mL and 5.6 µg/mL, whereas others displayed varied activity with IC50 values ranging from 7.0 µg/mL to 22.0 µg/mL. Both 11 and 10 showed greater susceptibility towards mature-stage trophozoites than ring-stage parasites. The hemolytic and in vitro cytotoxicity assays revealed that compounds 11 and 10 did not cause any toxic effects on host red blood cells (uninfected), human-derived Mo7e cells, and murine-derived BA/F3 cells. The in vitro observations are consistent with the in silico studies using P. falciparum-dihydrofolate reductase, where 11 and 10 showed a binding affinity of -10.4 Kcal/mol. This is the first report of the hybrid scaffold, 4-nitrobenzenesulfonamide chalcones, demonstrating its potential as an anti-plasmodial agent.

Exploring Macroscopic Dipoles of Designed Cyclic Peptide Ordered Assemblies to Harvest Piezoelectric Properties.

Crystalline materials exhibiting non-centrosymmetry and possessing substantial surface dipole moments play a critical role in piezoelectricity. Designing biocompatible self-assembled materials with these attributes is particularly challenging when compared to inorganic materials and ceramics. In this study, we elucidate the crystal conformations of novel cyclic peptides that exhibit self-assembly into tubular structures characterized by unidirectional hydrogen bonding and piezoelectric properties. Unlike cyclic peptides derived from alternating L- and D-amino acids, those derived from new δ-amino acids demonstrate the formation of self-assembled tubes with unidirectional hydrogen bonds. Further, the tightly packed tubular assemblies and higher macrodipole moments result in superior piezoelectric coefficients compared to peptides with lower macrodipole moments. Our findings underscore the potential for designing cyclic peptides with unidirectional hydrogen bonds, thereby paving the way for their application in design of biocompatible piezo- and ferroelectric materials.

Automated Regularized Deconvolution for Eliminating Extra-Column Effects in Fast High-Efficiency Separations.

With the introduction of ultrahigh efficiency columns and fast separations, the need to eliminate peak deformation contributed by the instrument must be effectively solved. Herein, we develop a robust framework to automate deconvolution and minimize its artifacts, such as negative dips, wild noise oscillations, and ringing, by combining regularized deconvolution and Perona-Malik (PM) anisotropic diffusion methods. A asymmetric generalized normal (AGN) function is proposed to model the instrumental response for the first time. With no-column data at various flow rates, the interior point optimization algorithm extracts the parameters describing instrumental distortion. The column-only chromatogram was reconstructed using the Tikhonov regularization technique with minimal instrumental distortion. For illustration, four different chromatography systems are used in fast chiral and achiral separations with 2.1 and 4.6 mm i.d. columns. Ordinary HPLC data can approach highly optimized UHPLC data. Similarly, in fast HPLC-circular dichroism (CD) detection, 8000 plates were gained for a fast chiral separation. Moment analysis of deconvolved peaks confirms correction of the center of mass, variance, skew, and kurtosis. This approach can be easily integrated and used with virtually any separation and detection system to provide enhanced analytical data.

Foldamer Nanotubes Mediated Label-Free Detection of Protein-Small Molecule Interactions.

Development of miniaturized lab-on-chip devices for the detection of rapid and specific small molecule-protein binding interactions at very low concentrations holds significant importance in drug discovery and biomedical applications. Here, the label-free detection of small molecule-protein interactions is reported on the surface functionalizable nanotubes of α,γ-hybrid peptide helical foldamers using nanoscale capacitance and impedance spectroscopy. The 12-helix conformation of the α,γ-hybrid peptide observed in the single crystals, self-assembled into nanotubes in an aqueous environment with exposed cysteine thiols for small molecule conjugation. The binding of streptavidin to the covalently linked biotin on the surface of nanotubes was detected at the picomolar concentrations. No change in the capacitance and impedance were observed in the absence of either immobilized biotin or protein streptavidin. The functionalizable hybrid peptide nanotubes reported here pave the way for the label-free detection of various small molecule protein interactions at very low concentrations.

Anion Tuned Structural Modulation and Nonlinear Optical Effects of Metal-Ion Directed 310 -Helix Networks.

Metals play an important role in the structure and functions of various proteins. The combination of metal ions and peptides have been emerging as an attractive field to create advanced structures and biomaterials. Here, we are reporting the anion-influenced, silver ion coordinated diverse networks of designed short tripeptide 310 -helices with terminal pyridyl groups. The short peptides adopted classical right-handed, left-handed and 310 EL -helical conformations in the presence of different silver salts. The peptides have displayed conformational flexibility to accommodate different sizes and interactions of anions to yield a variety of metal-coordinated networks. The complexes of metal ions and peptides have shown different porous networks, right- and left-handed helical polymers, transformation of helix into superhelix and 2 : 2 metal-peptide macrocycles. Further, the metal-peptide crystals with inherent dipoles of helical peptides gave striking second harmonic generation response. The optical energy upconversion from NIR to red and green light is demonstrated. Overall, we have shown the utilization of short 310 -helices for the construction of diverse metal-coordinated helical networks and notable non-linear optical effects.

On the parameter estimation of Box-Cox transformation cure model.

We propose an improved estimation method for the Box-Cox transformation (BCT) cure rate model parameters. Specifically, we propose a generic maximum likelihood estimation algorithm through a non-linear conjugate gradient (NCG) method with an efficient line search technique. We then apply the proposed NCG algorithm to BCT cure model. Through a detailed simulation study, we compare the model fitting results of the NCG algorithm with those obtained by the existing expectation maximization (EM) algorithm. First, we show that our proposed NCG algorithm allows simultaneous maximization of all model parameters unlike the EM algorithm when the likelihood surface is flat with respect to the BCT index parameter. Then, we show that the NCG algorithm results in smaller bias and noticeably smaller root mean square error of the estimates of the model parameters that are associated with the cure rate. This results in more accurate and precise inference on the cure rate. In addition, we show that when the sample size is large the NCG algorithm, which only needs the computation of the gradient and not the Hessian, takes less CPU time to produce the estimates. These advantages of the NCG algorithm allows us to conclude that the NCG method should be the preferred estimation method over the already existing EM algorithm in the context of BCT cure model. Finally, we apply the NCG algorithm to analyze a well-known melanoma data and show that it results in a better fit when compared to the EM algorithm.

Viral inhibitory potential of hyoscyamine in Japanese encephalitis virus-infected embryonated chicken eggs involving multiple signaling pathways.

Japanese encephalitis virus (JEV) is the leading cause of viral encephalitis worldwide. The emergence of new genotypes of the virus and a high rate of mutation make it necessary to develop alternative treatment strategies against this deadly pathogen. Although the antiviral properties of Atropa belladonna and some of its active components, such as atropine and scopolamine, have been studied, the effect of another important component, hyoscyamine, against JEV infection has not yet been investigated. In this study, we investigated the antiviral effect of hyoscyamine against JEV and its immunomodulatory activity in embryonated chicken eggs. Pretreatment with hyoscyamine sulphate resulted in a significant decrease in the viral load in both chorioallantoic membrane (CAM) and brain tissues at 48 and 96 hours postinfection. In silico studies showed stable binding and interaction between hyoscyamine and non-structural protein 5 (NS5), suggesting that this could be the basis of its antiviral effect. Embryonated eggs pretreated with hyoscyamine sulphate showed upregulation of Toll-like receptor 3 (TLR3), TLR7, TLR8, interleukin 4 (IL-4), and IL-10 as well as interferons and regulatory factors. Hyoscyamine sulphate was also found to cause significant downregulation of TLR4. The potential use of hyoscyamine for controlling JEV replication and its dissemination to the brain suggest that it may be a promising therapy option against JEV in the future.

A toxicological evaluation for safety assessment of ruthenium-based diosmetin complex in rats.

The flavonoid-based organometallic complexes have been identified as novel bioactive compounds with enhanced pharmacological and therapeutic activity. In this study, the ruthenium-p-cymene diosmetin complex was synthesized, characterized, and investigated for toxicological profiling through different toxicological and genotoxicological studies which include acute and sub-acute toxicity, chromosomal aberration, and bone marrow micronucleus study. The acute oral toxicity study demonstrated the LD50 dose of the complex at 500 mg/kg body weight which further instigated the sub-acute doses i.e. 50, 100, and 200 mg/kg. The histopathological analysis demonstrated that the 400 mg/kg dose was associated with severe toxicological incidences of the vital organs (liver, kidney, pancreas, testis, and stomach) except the ovary with increased levels of ALP, AST, ALT, and WBC count. However, 50, 100, and 200 mg/kg doses did not show any toxicological alteration and maintained the normal levels of hematological and serum biochemical parameters. The genotoxicological assessment of the complex depicted no such genetic damage or mutagenicity in any complex treated groups. In conclusion, the 50, 100, and 200 mg/kg doses were determined as therapeutic dose of the novel ruthenium-p-cymene diosmetin complex without any genotoxic and mutagenic potential which can be further implemented in the investigation of various pharmacological and therapeutic interventions.

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.

Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep learning of head CT scans and clinical information to predict long-term outcomes after sTBI. Materials and Methods This was a retrospective analysis of two prospectively collected databases. The model-building set included 537 patients (mean age, 40 years ± 17 [SD]; 422 men) from one institution from November 2002 to December 2018. Transfer learning and curriculum learning were applied to a convolutional neural network using admission head CT to predict mortality and unfavorable outcomes (Glasgow Outcomes Scale scores 1-3) at 6 months. This was combined with clinical input for a holistic fusion model. The models were evaluated using an independent internal test set and an external cohort of 220 patients with sTBI (mean age, 39 years ± 17; 166 men) from 18 institutions in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study from February 2014 to April 2018. The models were compared with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model and the predictions of three neurosurgeons. Area under the receiver operating characteristic curve (AUC) was used as the main model performance metric. Results The fusion model had higher AUCs than did the IMPACT model in the prediction of mortality (AUC, 0.92 [95% CI: 0.86, 0.97] vs 0.80 [95% CI: 0.71, 0.88]; P < .001) and unfavorable outcomes (AUC, 0.88 [95% CI: 0.82, 0.94] vs 0.82 [95% CI: 0.75, 0.90]; P = .04) on the internal data set. For external TRACK-TBI testing, there was no evidence of a significant difference in the performance of any models compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.90) in the prediction of mortality. The Imaging model (AUC, 0.73; 95% CI: 0.66-0.81; P = .02) and the fusion model (AUC, 0.68; 95% CI: 0.60, 0.76; P = .02) underperformed as compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.89) in the prediction of unfavorable outcomes. The fusion model outperformed the predictions of the neurosurgeons. Conclusion A deep learning model of head CT and clinical information can be used to predict 6-month outcomes after severe traumatic brain injury. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Haller in this issue.

Apple polyphenol phloretin complexed with ruthenium is capable of reprogramming the breast cancer microenvironment through modulation of PI3K/Akt/mTOR/VEGF pathways.

Our recent investigation directed to synthesize a novel ruthenium-phloretin complex accompanied by the study of antioxidant in addition to DNA binding capabilities, to determine the chemotherapeutic activity against breast carcinoma in vitro and in vivo. Ruthenium-phloretin complex was synthesized and characterized by different spectroscopic methods. The complex was further investigated to determine its efficacy in both MCF-7 and MDA-MB-231 human carcinoma cell lines and finally in an in vivo model of mammary carcinogenesis induced by DMBA in rats. Our studies confirm that the chelation of the metal and ligand was materialize by the 3-OH and 9-OH functional groups of the ligand and the complex is found crystalline and was capable of intercalating with CT-DNA. The complex was capable of reducing cellular propagation and initiate apoptotic events in MCF-7 and MDA-MB-231 breast carcinoma cell lines. Ruthenium-phloretin complex could modulate p53 intervene apoptosis in the breast carcinoma, initiated by the trail of intrinsic apoptosis facilitated through Bcl2 and Bax and at the same time down regulating the PI3K/Akt/mTOR pathway coupled with MMP9 regulated tumor invasive pathways. Ruthenium-phloretin chemotherapy could interrupt, revoke or suspend the succession of breast carcinoma by altering intrinsic apoptosis along with the anti-angiogenic pathway.

In-vivo protein nitration facilitates Vibrio cholerae cell survival under anaerobic, nutrient deprived conditions.

Protein tyrosine nitration (PTN), a highly selective post translational modification, occurs in both prokaryotic and eukaryotic cells under nitrosative stress. However, its physiological function is not yet clear. Like many gut pathogens, Vibrio cholerae also faces nitrosative stress, which makes its proteome more vulnerable to PTN. Here, we report for the first time in-vivo PTN in V. cholerae by immunoblotting and LC-ESI-MS/MS proteomic analysis. Our results indicated that in-vivo PTN in V. cholerae was culture media independent. Surprisingly, in-vivo PTN was reduced in V. cholerae proteome under anaerobic or hypoxic condition in a nutrient deprived state. Interestingly, intracellular nitrate content was more than the nitrite content in V. cholerae under anaerobic conditions. Additionally, biochemical measurement of GSH/GSSG ratio, activities of catalase and SOD, ROS and RNS imaging by confocal microscopy confirmed a relative intracellular oxidizing environment in V. cholerae under anaerobic conditions. This altered redox environment favors the oxidation of nitrite which may be generated from protein denitration enriching the intracellular nitrate pool. The cell survival of V. cholerae can finally be facilitated by nitrate reductase (NapA) utilizing that nitrate pool. Our cell viability study using wild type and ΔnapA strain of V. cholerae also supported the role of NapA mediated cell survival under nutrient deprived anaerobic conditions. In spite of having nitrate reductase (NapA), V. cholerae lacks any nitrite reductase (NiR). Hence, in-vivo nitration may provide an avenue for toxic nitrite storage and also may help in nitrosative stress tolerance mechanism preventing further unnecessary protein nitration in V. cholerae proteome.

A Fokker-Planck feedback control framework for optimal personalized therapies in colon cancer-induced angiogenesis.

In this paper, a new framework for obtaining personalized optimal treatment strategies in colon cancer-induced angiogenesis is presented. The dynamics of colon cancer is given by a Itó stochastic process, which helps in modeling the randomness present in the system. The stochastic dynamics is then represented by the Fokker-Planck (FP) partial differential equation that governs the evolution of the associated probability density function. The optimal therapies are obtained using a three step procedure. First, a finite dimensional FP-constrained optimization problem is formulated that takes input individual noisy patient data, and is solved to obtain the unknown parameters corresponding to the individual tumor characteristics. Next, a sensitivity analysis of the optimal parameter set is used to determine the parameters to be controlled, thus, helping in assessing the types of treatment therapies. Finally, a feedback FP control problem is solved to determine the optimal combination therapies. Numerical results with the combination drug, comprising of Bevacizumab and Capecitabine, demonstrate the efficiency of the proposed framework.

Developing a Mathematical Model of Intracellular Calcium Dynamics for Evaluating Combined Anticancer Effects of Afatinib and RP4010 in Esophageal Cancer.

Targeting dysregulated Ca2+ signaling in cancer cells is an emerging chemotherapy approach. We previously reported that store-operated Ca2+ entry (SOCE) blockers, such as RP4010, are promising antitumor drugs for esophageal cancer. As a tyrosine kinase inhibitor (TKI), afatinib received FDA approval to be used in targeted therapy for patients with EGFR mutation-positive cancers. While preclinical studies and clinical trials have shown that afatinib has benefits for esophageal cancer patients, it is not known whether a combination of afatinib and RP4010 could achieve better anticancer effects. Since TKI can alter intracellular Ca2+ dynamics through EGFR/phospholipase C-γ pathway, in this study, we evaluated the inhibitory effect of afatinib and RP4010 on intracellular Ca2+ oscillations in KYSE-150, a human esophageal squamous cell carcinoma cell line, using both experimental and mathematical simulations. Our mathematical simulation of Ca2+ oscillations could fit well with experimental data responding to afatinib or RP4010, both separately or in combination. Guided by simulation, we were able to identify a proper ratio of afatinib and RP4010 for combined treatment, and such a combination presented synergistic anticancer-effect evidence by experimental measurement of intracellular Ca2+ and cell proliferation. This intracellular Ca2+ dynamic-based mathematical simulation approach could be useful for a rapid and cost-effective evaluation of combined targeting therapy drugs.

Sparsity-based nonlinear reconstruction of optical parameters in two-photon photoacoustic computed tomography.

We present a new nonlinear optimization approach for the sparse reconstruction of single-photon absorption and two-photon absorption coefficients in the photoacoustic computed tomography (PACT). This framework comprises of minimizing an objective functional involving a least squares fit of the interior pressure field data corresponding to two boundary source functions, where the absorption coefficients and the photon density are related through a semi-linear elliptic partial differential equation (PDE) arising in PAT. Further, the objective functional consists of an L 1 regularization term that promotes sparsity patterns in absorption coefficients. The motivation for this framework primarily comes from some recent works related to solving inverse problems in acousto-electric tomography and current density impedance tomography. We provide a new proof of existence and uniqueness of a solution to the semi-linear PDE. Further, a proximal method, involving a Picard solver for the semi-linear PDE and its adjoint, is used to solve the optimization problem. Several numerical experiments are presented to demonstrate the effectiveness of the proposed framework.

Visible-Light Promoted C-O Bond Formation with an Integrated Carbon Nitride-Nickel Heterogeneous Photocatalyst.

Ni-deposited mesoporous graphitic carbon nitride (Ni-mpg-CNx ) is introduced as an inexpensive, robust, easily synthesizable and recyclable material that functions as an integrated dual photocatalytic system. This material overcomes the need of expensive photosensitizers, organic ligands and additives as well as limitations of catalyst deactivation in the existing photo/Ni dual catalytic cross-coupling reactions. The dual catalytic Ni-mpg-CNx is demonstrated for C-O coupling between aryl halides and aliphatic alcohols under mild condition. The reaction affords the ether product in good-to-excellent yields (60-92 %) with broad substrate scope, including heteroaryl and aryl halides bearing electron-withdrawing, -donating and neutral groups. The heterogeneous Ni-mpg-CNx can be easily recovered from the reaction mixture and reused over multiple cycles without loss of activity. The findings highlight exciting opportunities for dual catalysis promoted by a fully heterogeneous system.