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A computational methodology based on supervised machine learning (ML) is described for characterizing and designing anisotropic refractory composite alloys with desired thermal conductivities (TCs). The structural design variables are parameters of our fast computational microstructure generator, which were linked to the physical properties. Based on the Sobol sequence, a sufficiently large dataset of artificial microstructures with a fixed volume fraction (VF) was created. The TCs were calculated using our previously developed fast Fourier transform (FFT) homogenization approach. The resulting dataset was used to train our optimal autoencoder, establishing the intricate links between the material’s structure and properties. Specifically, the trained ML model’s inverse design of tungsten-30% (VF) copper with desired TCs was investigated. According to our case studies, our computational model accurately predicts TCs based on two perpendicular cut-section images of the experimental microstructures. The approach can be expanded to the robust inverse design of other material systems based on the target TCs.

BackgroundWe aimed to assess the feasibility of a dose painting (DP) procedure, known as simultaneous integrated boost intensity modulated radiation Therapy (SIB-IMRT), for treating prostate cancer with dominant intraprostatic lesions (DILs) based on multi-parametric magnetic resonance (mpMR) images and hierarchical clustering with a machine learning technique.MethodsThe mpMR images of 120 patients were used to create hierarchical clustering and draw a dendrogram. Three clusters were selected for performing agglomerative clustering. Then, the DIL acquired from the mpMR images of 20 patients were categorized into three groups to have them treated with a DP procedure being composed of three planning target volumes (PTVs) determined as PTV1, PTV2, and PTV3 in treatment plans. The DP procedure was carried out on the patients wherein a total dose of 80, 85 and 91 Gy were delivered to the PTV1, PTV2, and PTV3, respectively. Dosimetric and radiobiologic parameters [Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP)] of the DP procedure were compared with those of the conventional IMRT and Three-Dimensional Conformal Radiation Therapy (3DCRT) procedures carried out on another group of 20 patients. A post-treatment follow-up was also made four months after the radiotherapy procedures.ResultsAll the dosimetric variables and the NTCPs of the organs at risks (OARs) revealed no significant difference between the DP and IMRT procedures. Regarding the TCP of three investigated PTVs, significant differences were observed between the DP versus IMRT and also DP versus 3DCRT procedures. At post-treatment follow-up, the DIL volumes and apparent diffusion coefficient (ADC) values in the DP group differed significantly (p-value p-value > 0.05) between the DP versus IMRT.ConclusionsThe results of this comprehensive clinical trial illustrated the feasibility of our DP procedure for treating prostate cancer based on mpMR images validated with acquired patients’ dosimetric and radiobiologic assessment and their follow-ups. This study confirms significant potential of the proposed DP procedure as a promising treatment planning to achieve effective dose escalation and treatment for prostate cancer.Trial registration: IRCT20181006041257N1; Iranian Registry of Clinical Trials, Registered: 23 October 2019,https://en.irct.ir/trial/34305.

BackgroundWe aimed to assess the feasibility of a novel dose painting (DP) procedure for treating prostate cancer with dominant intraprostatic lesions (DILs) based on mpMR images and hierarchical clustering with a machine learning technique. MethodsThe mpMR images of 120 patients were used to create hierarchical clustering and draw a dendrogram. Three clusters were selected for performing agglomerative clustering. Then, the DIL acquired from the mpMR images of 20 patients were categorized into three groups to have them treated with a novel DP procedure being composed of three planning target volumes (PTVs) determined as PTV1, PTV2, and PTV3 in treatment plans. The DP procedure was carried out on the patients wherein a total dose of 80, 85, and 91 Gy were delivered to the PTV1, PTV2, and PTV3, respectively. Dosimetric and radiobiologic parameters (TCP & NTCP) of the DP procedure were compared with those of the conventional IMRT and 3DCRT procedures carried out on another group of 20 patients. A post-treatment follow-up was also made four months after the radiotherapy procedures.ResultsAll the dosimetric variables and the NTCPs of the organs at risks revealed no significant difference between the DP and IMRT procedures. Regarding the TCP of three investigated PTVs, significant differences were observed between the DP vs. IMRT and also DP vs. 3DCRT procedures. At post-treatment follow-up, the DIL volumes and ADC values in the DP group differed significantly (p-value0.05) between the DP vs. IMRT. ConclusionsThe results of this comprehensive clinical trial illustrated the feasibility of our novel DP procedure for treating prostate cancer based on mpMR images validated with acquired patients’ dosimetric and radiobiologic assessment and their follow-ups. This study confirms significant potential of the proposed DP procedure as a promising treatment planning to achieve effective dose escalation and treatment for prostate cancer.Trial registrationIRCT20181006041257N1; Iranian Registry of Clinical Trials, Registered: 23 Oct. 2019, https://en.irct.ir/trial/34305

Etiological investigations showed that vibrio is a Gram-negative, comma-shaped and facultative anaerobic bacterium, which is naturally found in marine environment. Vibrio causes many human diseases and often comes with foodborn gastroenteritis or diarrhea. Most of these foodborne infections are caused by V. Cholerae and V. parahaemolyticus and to a lesser extent create by V. vulnificus. Infection with these species is mostly due to the consumption of raw, improperly handled, under-processed and contaminated seafood like fish, shellfish and oyster. One of the biggest threats to public health is antibiotic resistance, such as resistance of vibrio species to a large number of antibiotics. This review focused on antibacterial activity of the world’s medicinal plants against vibrio species. Published articles were obtained from scientific databases including PubMed, Google scholar, Springer, Science Direct and scientific information database (SID) using following key words: vibrio, medicinal plant, essential oil and extract. Results of this literature have introduced some of the most important plants effective on vibrio spp., such as Thymus vulgaris (Thyme), Syzygium aromaticum (Clove), Zataria multiflora (Avishan shirazi), Zingiber officinale (Ginger), Punica granatum (Pomegranate), Satureja bachtiarica Bunge (Bakhtiari Savory), Mentha spicata (Spearmint), Cuminum cyminum (Cumin), Eucalyptus globulus (Blue gum), Camellia sinensis (Green tea), Rosmarinus officinalis (Rosemary) and Allium sativum (Garlic). Hence, phyto-therapy could be a suitable way to overcome the problem of development of the bacterial resistance to antibiotics.

A supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials with desired thermal conductivity (TC) is presented. The design variables are physical descriptors of the material microstructure that directly link microstructure to the material's properties. A sufficiently large and uniformly sampled database was generated based on the Sobol sequence. Microstructures were realized using an efficient dense packing algorithm, and the TCs were obtained using our previously developed Fast Fourier Transform (FFT) homogenization method. Our optimized ML method is trained over the generated database and establishes the complex relationship between the structure and properties. Finally, the application of the trained ML model in the inverse design of a new class of composite materials, liquid metal (LM) elastomer, with desired TC is discussed. The results show that the surrogate model is accurate in predicting the microstructure behavior with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies., 24 pages, 6 figures, 3 tables

The impact of strain or feed intake on food trace elements and its health risk assessment is still ambiguous, and therefore, available facts are rare. Therefore, this study aimed to assess the effects of both strains and feed intake on trace elements depositions to egg, toxic heavy metals concentration, and health risk assessment of egg consumption. In the current cross-sectional study, the selected strains, including Shaver White, Hy-Line W36, Bovanse White, Lohman LSL-Lite, and Native laying hens of Khorasan Razavi province, were examined. A total number of 50 samples of eggs and 15 samples of their feed was purchased from poultry farms. Yolk and white were separately analyzed. Sample preparation was performed by wet digestion followed by Inductively Coupled Plasma Mass Spectrometry. Trace metals Pb, As, Cd, Hg, Cr, and Ni were detected. Statistical analysis was performed in Stata11.2 portable software. Although there was a significant difference in strains and feed, no significant difference was observed in trace elements in egg contents (weight of egg white and egg yolk). Mercury concentration in all the samples was below the instrument detection limit. In this study, the target hazardous quotients were below one for all trace elements. Therefore, Iranian does not experience the adverse health effects due to the consumption of egg.