Confined batch foaming of semi-crystalline rubbery elastomers with carbon dioxide using a mold

Confined foaming of poly(ethylene-co-vinyl acetate-co-carbon monoxide) using carbon dioxide as a physical blowing agent in a mold with either permeable or impermeable boundaries has been explored as a strategy to control final foam dimensions and morphology. The results are discussed in terms of comparisons to free-foaming experiments conducted at the same pressure and temperature conditions following the same pressurization and depressurization paths. Foaming experiments were carried out at 30 and 40°C and 100, 200, and 300 bar followed by rapid depressurization of the foaming cell. Confined foaming led to smaller pores with more uniform distributions across the polymer cross-section. However, bulk foam densities of the foams generated under confinement were higher than those generated under the free-foaming mode. Surface characteristics and skin layer formation were altered by expansion against both the permeable and impermeable boundaries. Confined foaming promotes uniform pore distribution and overall dimensional uniformity and may impart surface texture but the trade-off is in the degree to which the bulk foam density can be lowered.     

Halting the hallmarks: a cellular automaton model of early cancer growth inhibition

Cancer treatment is a fragmented and varied process, as “cancer” is really hundreds of different diseases. The “hallmarks of cancer” proposed by Hanahan and Weinberg (Cell 100(1):57–70, 2000) are a framework for viewing cancer within a common set of underlying principles—ten properties that are common to almost all cancers, allowing them to grow uncontrollably and ravage the body. We used a cellular automaton model of tumour growth paired with lattice Boltzmann methods modelling oxygen flow to simulate combination drugs targeted at knocking out pairs of hallmarks. We found that knocking out some pairs of cancer-enabling hallmarks did not prevent tumour formation, while other pairs significantly prevent tumour growth (\(p=0.0004\) using Wilcoxon signed-rank adjusted with the Bonferroni correction for multiple comparisons). This is not what would be expected from models of knocking out the hallmarks individually, as many pairs did not have an additive effect but had either no statistically significant effect or a multiplicative one. We propose that targeting certain pairs of cancer hallmarks, specifically cancers ability to induce blood vessel development paired with another cancer hallmark, could prove an effective cancer treatment option.     

Spiral-shaped inertial stem cell device for high-throughput enrichment of iPSC-derived neural stem cells

Stem cell enrichment plays a critical role in both research and clinical applications. The typical method for stem cell enrichment may use invasive processes and takes a long period of time. Spiral-shaped microfluidic devices, which combine lift and Dean drag forces to direct cells of different sizes into separate trajectories, can be used to noninvasively process samples at a rate of milliliters per minute. This paper presents a simple 2-loop spiral-shaped inertial microfluidic devices with the aid of sheath flow to enrich neural stem cells (NSCs), derived from induced pluripotent stem cells. NSCs and spontaneously differentiated non-neural cells were mixed and flowed through the spiral-shaped devices. Samples collected at the outlets were analyzed for purity and recovery. It was found that the device focused the NSCs into a narrow trajectory, which could then be collected in two out of the eight outlets. The device was tested at different flow rates and found that the most highly enriched fractions (2.1×) with NSCs recovery 93% were achieved at the flow rate (3 ml/min). Next, we extended our investigation from 2-loop design to 10-loop design to eliminate the use of sheath flow. NSCs were enriched to 2.5×, but only 38% of the NSCs were recovered from the most enriched fractions. Spiral-shaped microfluidic devices are capable of rapid, label-free enrichment of target stem cells, and have great potential in point-of-care tissue preparation.     

Role of Nucleotide Excision Repair in Cisplatin Resistance

Cisplatin is a chemotherapeutic drug used for the treatment of a number of cancers. The efficacy of cisplatin relies on its binding to DNA and the induction of cytotoxic DNA damage to kill cancer cells. Cisplatin-based therapy is best known for curing testicular cancer; however, treatment of other solid tumors with cisplatin has not been as successful. Pre-clinical and clinical studies have revealed nucleotide excision repair (NER) as a major resistance mechanism against cisplatin in tumor cells. NER is a versatile DNA repair system targeting a wide range of helix-distorting DNA damage. The NER pathway consists of multiple steps, including damage recognition, pre-incision complex assembly, dual incision, and repair synthesis. NER proteins can recognize cisplatin-induced DNA damage and remove the damage from the genome, thereby neutralizing the cytotoxicity of cisplatin and causing drug resistance. Here, we review the molecular mechanism by which NER repairs cisplatin damage, focusing on the recent development of genome-wide cisplatin damage mapping methods. We also discuss how the expression and somatic mutations of key NER genes affect the response of cancer cells to cisplatin. Finally, small molecules targeting NER factors provide important tools to manipulate NER capacity in cancer cells. The status of research on these inhibitors and their implications in cancer treatment will be discussed.     

De Novo Design of Polyhedral Protein Assemblies: Before and After the AI Revolution

Self-assembling polyhedral protein biomaterials have gained attention as engineering targets owing to their naturally evolved sophisticated functions, ranging from protecting macromolecules from the environment to spatially controlling biochemical reactions. Precise computational design of de novo protein polyhedra is possible through two main types of approaches: methods from first principles, using physical and geometrical rules, and more recent data-driven methods based on artificial intelligence (AI), including deep learning (DL). Here, we retrospect first principle- and AI-based approaches for designing finite polyhedral protein assemblies, as well as advances in the structure prediction of such assemblies. We further highlight the possible applications of these materials and explore how the presented approaches can be combined to overcome current challenges and to advance the design of functional protein-based biomaterials.     

The effectiveness of Light Rail transit in achieving regional CO<Subscript>2</Subscript> emissions targets is linked to building energy use: insights from system dynamics modeling

Cities worldwide face the challenges of accommodating a growing population, while reducing emissions to meet climate mitigation targets. Public transit investments are often proposed as a way to curb emissions while maintaining healthy urban economies. However, cities face a system-level challenge in that transportation systems have cascading effects on land use and economic development. Understanding how an improved public transit system could affect urban growth and emissions requires a system-level view of a city, to anticipate side effects that could run counter to policy goals. To address this knowledge gap, we conducted a case study on the rapidly growing Research Triangle, North Carolina (USA) region, which has proposed to build a Light Railway by 2026 along a heavily used transportation corridor between the cities of Durham and Chapel Hill. At the same time, Durham County has set a goal of lowering greenhouse gas emissions by 30% from a 2005 baseline by 2030. In collaboration with local stakeholders, we developed a system dynamics model to simulate how Light Rail transit and concurrent policies could help or hinder these sustainable growth goals. The Durham–Orange Light Rail Project (D–O LRP) model simulates urban–regional dynamics between 2000 and 2040, including feedbacks from energy spending on economic growth and from land scarcity on development. Counter to expectations, model scenarios that included Light Rail had as much as 5% higher regional energy use and CO₂ emissions than business-as-usual (BAU) by 2040 despite many residents choosing to use public transit instead of private vehicles. This was largely due to an assumption that Light Rail increases demand for commercial development in the station areas, creating new jobs and attracting new residents. If regional solar capacity grew to 640 MW, this would offset the emissions growth, mostly from new buildings, that is indirectly due to Light Rail. National trends in building and automobile energy efficiency, as well as federal emissions regulation under the Clean Power Plan, would also allow significant progress toward the 2030 Durham emissions reduction goal. By simulating the magnitude of technology and policy effects, the D–O LRP model can enable policy makers to make strategic choices about regional growth.     

"Factor structure of the BASC-2 Behavioral and Emotional Screening System Student Form": Correction to Dowdy et al. (2011).

Reports an error in "Factor structure of the BASC–2 Behavioral and Emotional Screening System Student Form" by E. Dowdy, et al. (Psychological Assessment. np). There was an omission in the author note. The author note should have included a disclosure as follows, “Randy W. Kamphaus wishes to disclose a potential conflict of interest in that he receives a portion of royalties on net sales of BESS.” (The following abstract of the original article appeared in record 2011-04578-001.) The BASC–2 Behavioral and Emotional Screening System (BESS) Student Form (Kamphaus & Reynolds, 2007) is a recently developed youth self-report rating scale designed to identify students at risk for behavioral and emotional problems. The BESS Student Form was derived from the Behavior Assessment System for Children—Second Edition Self-Report of Personality (BASC–2 SRP; Reynolds & Kamphaus, 2004) using principal component analytic procedures and theoretical considerations. Using 3 samples, the authors conducted exploratory factor analyses (EFA) and confirmatory factor analyses (CFA) to understand the underlying factor structure of the BESS Student Form. The results of the EFA suggested that the SRP contained a 4-factor (i.e., Personal Adjustment, Inattention/Hyperactivity, Internalizing, School Problems) emergent structure, which was supported by CFA in 2 additional samples. Practical and research implications are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Improvements in partner support predict sexual satisfaction among individuals with multiple sclerosis.

Purpose: Sexual dysfunction and low sexual satisfaction are common among individuals with multiple sclerosis (MS); however, little is known about factors that influence sexual satisfaction within this population. As such, the purpose of this study was to investigate the extent to which changes in negative and positive partner support predict sexual satisfaction levels over time in individuals with MS. Design: Eighty-one individuals with MS completed measures of sexual dysfunction, sexual satisfaction, partner social support, and depression. Data from baseline and posttreatment follow-up were obtained from a larger randomized clinical trial of telephone-administered psychotherapy for depression in a population with MS. Multiple regression analyses were conducted with change in overall sexual satisfaction from baseline to posttreatment as the outcome variable. Results: After controlling for age, gender, sexual dysfunction, years diagnosed with MS, and depression severity, those with increased positive partner support reported significant improvement in sexual satisfaction over time (β = .50, p