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The damage evolution mechanism of rocks is one of the most important aspects in studying of rock fatigue behavior. Fatigue damage evolution of three rock types (onyx marble, sandstone and soft limestone) with different brittleness were considered in the present study. Intensive experimental tests were conducted on the chosen rock samples and acoustic emission (AE) sensors were used in some of them to monitor the fracturing process. Experimental tests indicated that brittleness strongly influences damage evolution of rocks in the course of static and dynamic loading. AE monitoring revealed that micro-crack density induced by the applied loads during different stages of the failure processes increases as rock brittleness increases. Also, results of fatigue tests on the three rock types indicated that the rock with the most induced micro-cracks during loading cycles has the least fatigue life. Furthermore, the condition of failure surfaces of the studied rocks samples, subjected to dynamic and static loading, were evaluated and it was concluded that the roughness of failure surfaces is influenced by loading types and rock brittleness. Dynamic failure surfaces were rougher than static ones and low brittle rock demonstrate a smoother failure surface compared to high brittle rock.

This study examines the intricate task of predicting construction duration for drill-and-blast tunnels utilizing machine learning (ML) techniques. First, a comprehensive dataset (500 data points) encompassing 20 diverse parameters was compiled by constructing eight tunnels. After meticulous analysis, 17 of the 20 parameters were identified as crucial for training the algorithms. The overbreak and tunnel cross-section parameters were found to exert a significant influence on the tunnel construction duration. To enhance the predictive accuracy of the ML models, an intensive hyperparameter tuning process was conducted. The findings underscored the effectiveness of the Gaussian process regression model in capturing complex and nonlinear relationships, achieving an average R-squared of 0.89. Additionally, an ML-based graphical user interface (GUI) was developed to facilitate real‒time estimation of tunnel construction duration. This GUI not only enables initial predictions but also allows for dynamic updates throughout the construction phase, enhancing its practical utility.

In engineering design, the brittleness index (BI) seems to play a significant role in material selection, failure forecasting, and service performance. Determining BI is traditionally a laborious, expensive, and highly experimental process. This study gives a full framework that combines experimental tests with machine learning methods to model the brittleness behavior of the heavy-weight cement slurries. The study is based on different experimental tests, such as Split Hopkinson pressure bar Test, Uniaxial Compressive Strength Test, Brazilian Test and etc. on mechanical and physical properties of ten different slurry formulations and a dataset of 250 experimental observations from these formulations, each with 14 independent input parameters. Fourteen machine learning models were created, and their accuracy and dependability were statistically compared, and a new Equation has been developed for estimating the BI based on the test results. Gaussian process regression and support vector regression were the two most accurate models based on interaction with each of the models; when using the full set of input variables, their R 2 values ranged from 0.93 to 0.97. When variable selections were applied to the final models, the number of features taken into consideration was lowered to eight, which resulted in further accuracy improvements and R 2 values ranging from 0.940 to 0.990. Even though every factor affected the BI, EP had the biggest impact; the main goal of some additives was to lessen brittleness. By using more sophisticated machine learning algorithms, this research provides a new method for predicting business intelligence, which lowers the time and expense required and improves decision-making.

Most rocks in nature are porous and usually saturated with different fluids such as water, oil, and gas. The conventional and unconventional hydrocarbon reservoirs are mainly associated with sedimentary formations. The main rocks of these reservoirs are sandstone (porous rock), limestone and oil shale (tight rock), which are associated with varying porosity. Hence, porosity serves as a fundamental parameter for most reservoir rocks. In this research, the effect of porosity on the mechanical behaviour of geo-materials and its fatigue behaviour was investigated. For this purpose, a total of five geo-material sample groups with varying porosities were prepared and designated, i.e. groups A, B, C, D, and E. Group A exhibited the highest porosity 20-21.5%, while group E had the lowest porosity 2-3%, respectively. The conventional quasi-static strength tests and cycle loading tests with constant frequency and amplitude were performed on the samples and different results were obtained. The samples belonging to group E, with the lowest porosity (2-3%), exhibited the highest mechanical strength, elastic modulus and fracture toughness values and the lowest Poisson's ratio compared to those of higher porosity samples. During the cyclic loading period, the fatigue stress-life graph of the E group has the lowest slope compared to the other groups. It means that the slope of the graph increases as the porosity increases in all groups. Therefore, the E group has the lowest porosity and the longest fatigue life time, i.e. porosity and fatigue resistance have an inverse correlation.

Ibrutinib inhibits Bruton tyrosine kinase while venetoclax is a specific inhibitor of the anti-apoptotic protein BCL2. Both drugs are highly effective as monotherapy against chronic lymphocytic leukemia (CLL), and clinical trials using the combination therapy have produced remarkable results in terms of rate of complete remission and frequency of undetectable minimal residual disease. However, the laboratory rationale behind the success of the drug combination is still lacking. A better understanding of how these two drugs synergize would eventually help develop other rational combination strategies. Using an ex vivo model that promotes CLL proliferation, we show that modeled ibrutinib proliferative responses, but not viability responses, correlate well with patients’ actual clinical responses. Importantly, we demonstrate for the first time that ibrutinib and venetoclax act on distinct CLL subpopulations that have different proliferative capacities. While the dividing subpopulation of CLL responds to ibrutinib, the resting subpopulation preferentially responds to venetoclax. The combination of these targeted therapies effectively reduced both the resting and dividing subpopulations in most cases. Our laboratory findings help explain several clinical observations and contribute to the understanding of tumor dynamics. Additionally, our proliferation model may be used to identify novel drug combinations with the potential of eradicating residual disease.

More than 70% of Epstein–Barr virus (EBV)-negative Hodgkin lymphoma (HL) cases display inactivation of TNFAIP3 (A20), a ubiquitin-editing protein that regulates nonproteolytic protein ubiquitination, indicating the significance of protein ubiquitination in HL pathogenesis. However, the precise mechanistic roles of A20 and the ubiquitination system remain largely unknown in this disease. Here, we performed high-throughput CRISPR screening using a ubiquitin regulator-focused single-guide RNA library in HL lines carrying either wild-type or mutant A20. Our CRISPR screening highlights the essential oncogenic role of the linear ubiquitin chain assembly complex (LUBAC) in HL lines, which overlaps with A20 inactivation status. Mechanistically, LUBAC promotes IKK/NF-κB activity and NEMO linear ubiquitination in A20 mutant HL cells, which is required for prosurvival genes and immunosuppressive molecule expression. As a tumor suppressor, A20 directly inhibits IKK activation and HL cell survival via its C-terminal linear-ubiquitin binding ZF7. Clinically, LUBAC activity is consistently elevated in most primary HL cases, and this is correlated with high NF-κB activity and low A20 expression. To further understand the complete mechanism of NF-κB activation in A20 mutant HL, we performed a specifically designed CD83-based NF-κB CRISPR screen which led us to identify TAK1 kinase as a major mediator for NF-κB activation in cells dependent on LUBAC, where the LUBAC-A20 axis regulates TAK1 and IKK complex formation. Finally, TAK1 inhibitor Takinib shows promising activity against HL in vitro and in a xenograft mouse model. Altogether, these findings provide strong support that targeting LUBAC or TAK1 could be attractive therapeutic strategies in A20 mutant HL.

One of the crucial tasks during the EPB shield tunnelling is estimating the optimum tunnel face pressure (FP), which ensures self-drilling safety, helps to reduce surface settlement and prevents the entire tunnel from collapsing. This study aims to propose an optimized and state-of-the-art machine learning model to predict the EPB-FP as accurately as possible. To this end, a support vector regression SVR model and six metaheuristic optimization algorithms of particle swarm optimization (PSO), grey wolf optimization (GWO), multiverse optimization (MVO), moth flame optimization (MFO), sine cosine algorithm (SCA), and social spider optimization (SSO) were developed to predict the FP in the EPB tunnelling. 250 data sets, including seven input parameters and one output parameter (FP) were utilized in the models obtained from the Tehran metro Line 3. Finally, the performance prediction of the models from high to low was SVR–PSO,SVR–GWO,SVR–MVO,SVR–MFO,SVR–SCA,SVR–SSO, and SVR with ranking scores of 55,49,45,39,37,30, and 21, respectively. Therefore, the SVR–PSO hybrid model produced the most accurate results and it was recommended to predict the FP in the EPB tunnelling. In addition, using the mutual information test, the surface load (SL) parameter was identified as the most influential parameter on the FP. This work’s significance is that it allows geotechnical engineers to accurately estimate the FP during the EPB tunnelling, which ensures the safety of the excavation itself, helps to minimize surface settlement, and ultimately prevents the collapse of the entire tunnel. Also, it can prevent the time-consuming and cost overruns that the FP may cause during the EPB tunnelling.HighlightsImprove the SVR ability through meta-heuristic optimization for low data.Develop six hybrid meta-heuristic algorithms to predict the tunnel face pressure.High accuracy in the prediction of face pressure during EPB tunnelling.Sensitivity analysis of the input parameters using mutual information testRecognition of the most robust model.

Liquid biopsy is a valuable tool in advanced and metastatic cancers for detection of genomic alterations in tumors that facilitate personalized targeted therapy approaches. Analyzing circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) provides an opportunity to detect tumor genomic changes during therapy and capture inter- and intra-heterogeneity of genomically divergent cancer cell evolution. Herein, we present a patient with metastatic castration-resistant prostate cancer, with progression to soft tissues, bone, and regional lymph nodes, who was treated with abiraterone plus prednisone, with excellent prostate-specific antigen response. At the time of progression, NGS analysis of ctDNA using the FoundationOne ® Liquid test revealed a CHEK2 mutation and a BRAF V600E mutation, the latter being exceedingly rare in prostate cancer. At the time of biochemical recurrence, the patient was referred to hematology for evaluation of chronic but stable thrombocytopenia prior to initiating new systemic therapy. Results of a bone marrow biopsy were consistent with hairy-cell leukemia, where the BRAF V600E mutation is considered the disease-defining mutation detectable in nearly all cases at diagnosis. In this case, liquid biopsy served as a noninvasive, highly sensitive approach to help reveal tumor genomic heterogeneity but also identified an unexpected genomic alteration leading to secondary cancer diagnosis and changes to treatment-related decision-making.

The association of warm autoimmune hemolytic anemia (wAIHA) with various lymphoproliferative disorders is well reported in the literature. But the association of wAIHA with T-cell prolymphocytic leukemia (T-PLL), a very rare lymphoproliferative disorder, has never been reported. A 71-year-old man was in his usual state of health until three years ago when he developed intermittent bouts of worsening anemia associated with mild peripheral blood lymphocytosis. He was diagnosed with wAIHA and steroid therapy was initiated, resulting in an improvement in the hemoglobin level of the patient. His lymphocyte count remained persistently elevated but he did not develop any malignancy-related signs or symptoms. A diagnosis of 'indolent' T-cell prolymphocytic leukemia (small cell variant) was made by combining distinctive clinical, morphologic, immunophenotypic, and cytogenetic analysis. His wAIHA went into complete remission and steroid therapy was successfully tapered off. He has not required any treatment for his T-PLL during the last two years' follow-up.