Human Pluripotent Stem Cells in Cardiovascular Research and Regenerative Medicine

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4-Bromo-2-[1-(4-eth­oxy­phen­yl)-1-methyl­eth­yl]-1-methyl­benzene

In title compound, C18H21BrO, the dihedral angle between two rings is 85.72°. No classical hydrogen bonds are found and only van der Waals forces stabilize the crystal packing

A benchmark test problem toolkit for multi-objective path optimization

Due to the complexity of multi-objective optimization problems (MOOPs) in general, it is crucial to test MOOP methods on some benchmark test problems. Many benchmark test problem toolkits have been developed for continuous parameter/numerical optimization, but fewer toolkits reported for discrete combinational optimization. This paper reports a benchmark test problem toolkit especially for multi-objective path optimization problem (MOPOP), which is a typical category of discrete combinational optimization. With the reported toolkit, the complete Pareto front of a generated test problem of MOPOP can be deduced and found out manually, and the problem scale and complexity are controllable and adjustable. Many methods for discrete combinational MOOPs often only output a partial or approximated Pareto front. With the reported benchmark test problem toolkit for MOPOP, we can now precisely tell how many true Pareto points are missed by a partial Pareto front, or how large the gap is between an approximated Pareto front and the complete one

Integrating Complementary Medicine Into the Care of Childhood Cancer Survivors: A Brief Report on the Preliminary Framework and Implementation of an Educational Program

BackgroundExisting educational programs typically include limited information on traditional, complementary, and integrative medicine (TCIM) for survivors of childhood cancer.ObjectivesThis brief report presents the preliminary results of an educational program that aims to promote the safe and effective use of Chinese medicine (CM) among survivors in Hong Kong.MethodsSurvivors of childhood cancer, their caregivers, and oncology practitioners were invited to participate in a program that consists of two didactic seminars and a written educational booklet that disseminated information on the use of CM. A structured questionnaire was used to evaluate participants' receptivity toward and perceived relevance of the program. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to discuss the impact of the intervention.ResultsReach: A total of 174 participants attended the seminars, and the seminar recording received over 380 views on social media platforms since April 2021. The hardcopy of the educational booklet was distributed to 43 recipients. The web-version of the booklet was sent to 67 participants and downloaded 143 times. Efficacy: The majority found that the content of the seminar useful (mean score = 5.04/6 points), especially the CM exercise (mean score = 4.88/6 points) and dietary advice (mean score = 4.99/6 points). Intention to adopt: The survivors (or their caregivers) reported that they would adopt advice on food therapies (83.3%) and traditional Chinese health exercises (55.6%) during survivorship.ConclusionThe preliminary data on patient preferences will be applied to further develop educational materials and to establish a TCIM referral network within the cancer survivor community

Direct prediction of phonon density of states with Euclidean neural networks

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here we demonstrate the direct prediction of phonon density of states using only atomic species and positions as input. We apply Euclidean neural networks, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of $\sim 10^{3}$ examples with over 64 atom types. Our predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements,and is naturally suited to efficiently predict alloy systems without additional computational cost. We demonstrate the potential of our network by predicting a broad number of high phononic specific heat capacity materials. Our work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors.Comment: 21 pages total, 5 main figures + 16 supplementary figures. To appear in Advanced Science (2021

Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner

Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexity with respect to system sizes leads to prohibitive computational costs when the simulation contains over a few hundred atoms in practice. We present an In-Distribution substructure Embedding Active Learner (IDEAL) to enable efficient simulation of large complex systems with quantum accuracy by maintaining a machine learning force field (MLFF) as an accurate surrogate to the first principles methods. By extracting high-uncertainty substructures into low-uncertainty atom environments, the active learner is allowed to concentrate on and learn from small substructures of interest rather than carrying out intractable quantum chemical computations on large structures. IDEAL is benchmarked on various systems and shows sub-linear complexity, accelerating the simulation thousands of times compared with conventional active learning and millions of times compared with pure first principles simulations. To demonstrate the capability of IDEAL in practical applications, we simulated a polycrystalline lithium system composed of one million atoms and the full ammonia formation process in a Haber-Bosch reaction on a 3-nm Iridium nanoparticle catalyst on a computing node comprising one single A100 GPU and 24 CPU cores

Co-Targeting Plk1 and DNMT3a in Advanced Prostate Cancer

Because there is no effective treatment for late-stage prostate cancer (PCa) at this moment, identifying novel targets for therapy of advanced PCa is urgently needed. A new network-based systems biology approach, XDeath, is developed to detect crosstalk of signaling pathways associated with PCa progression. This unique integrated network merges gene causal regulation networks and protein-protein interactions to identify novel co-targets for PCa treatment. The results show that polo-like kinase 1 (Plk1) and DNA methyltransferase 3A (DNMT3a)-related signaling pathways are robustly enhanced during PCa progression and together they regulate autophagy as a common death mode. Mechanistically, it is shown that Plk1 phosphorylation of DNMT3a leads to its degradation in mitosis and that DNMT3a represses Plk1 transcription to inhibit autophagy in interphase, suggesting a negative feedback loop between these two proteins. Finally, a combination of the DNMT inhibitor 5-Aza-2\u27-deoxycytidine (5-Aza) with inhibition of Plk1 suppresses PCa synergistically