Review of additive manufactured tissue engineering scaffolds: relationship between geometry and performance

Material extrusion additive manufacturing has rapidly grown in use for tissue engineering research since its adoption in the year 2000. It has enabled researchers to produce scaffolds with intricate porous geometries that were not feasible with traditional manufacturing processes. Researchers can control the structural geometry through a wide range of customisable printing parameters and design choices including material, print path, temperature, and many other process parameters. Currently, the impact of these choices is not fully understood. This review focuses on how the position and orientation of extruded filaments, which sometimes referred to as the print path, lay-down pattern, or simply "scaffold design", affect scaffold properties and biological performance. By analysing trends across multiple studies, new understanding was developed on how filament position affects mechanical properties. Biological performance was also found to be affected by filament position, but a lack of consensus between studies indicates a need for further research and understanding. In most research studies, scaffold design was dictated by capabilities of additive manufacturing software rather than free-form design of structural geometry optimised for biological requirements. There is scope for much greater application of engineering innovation to additive manufacture novel geometries. To achieve this, better understanding of biological requirements is needed to enable the effective specification of ideal scaffold geometries

The Problems of Civil Law in China and Japan

千葉大学大学院人文社会科学研究科研究プロジェクト報告書第171集『中日における民法現代化の課題』 小賀野晶一

Sequence-Controlled Copolymerization of Salicylic <i>O</i>‑Carboxyanhydrides by Organocatalysts

The composition sequence of a copolymer is intricately linked to its physical and mechanical properties. However, achieving chemical selectivity for copolymerization monomers and controlling the sequence structure of copolymers remain a challenge. This study delves into a strategy that involves tuning the organocatalyst transition from hydrogen bonding interaction to ionic pair to realize copolymerization sequence control in one-pot reactions. Ring-opening copolymerization (ROCOP) of salicylic acid O-carboxyanhydride (SAOCA) and its analogous monomers, including 5-methylsalicylic acid O-carboxyanhydride (5-MeSAOCA) and 4-fluor-salicylic acid O-carboxyanhydride (4-FSAOCA), is successfully conducted in tetrahydrofuran (THF) at ambient conditions, resulting in narrow chain distributions (Đ < 1.25). This accomplishment is made possible by utilizing a catalytic combination of 1,8-diazabicyclo [5.4.0]undec-7-ene (DBU) and thioureas (TUs) with varying pKa values. The acidity of TU influences its interaction with DBU to generate the transition states of hydrogenbonding or ionic pair, while the quantity of TU affects the steric hindrance around the anion; both are crucial factors in regulating the sequence distribution of PSA-series copolymers ranging from random to block architecture

Areas of significant brain activations in the grammaticality judgment task.

<p>Notes: L, left hemisphere; R, right hemisphere. All activations reported were thresholded at cluster level p<0.05. * Averaged time course data from all the voxels within a sphere of 6 mm radius in each region of interests (PCu, INS, IFG, CN) were extracted for comparisons of BOLD signal changes with brain networks between the explicit and the implicit group.</p

Finite-state grammar used to generate grammatical syllable sequences for explicit and implicit learning.

<p>Grammatical syllable sequences are generated by starting at the leftmost ‘In’ state and then following a path of arrows until the rightmost ‘Out’ state is reached. For each arrow traversed, the indicated syllable is added to the syllable sequence. For example, <i>pok kun dem dem tik</i> is grammatical, and <i>pok kun tik guk pok</i> is ungrammatical.</p

Critical threshold for average weights () on networks with specified network size () and average degree ().

<p>Critical threshold for average weights () on networks with specified network size () and average degree ().</p

Brain networks for explicit and implicit learners during grammaticality judgment.

<p>Positive relationship were denoted by solid lines and negative relationships by dotted lines. See text for further explanation.</p

Role of working memory in explicit learning.

<p>Working memory capacity in the explicit group showed a positive correlation with behavioral cognitive performance, as seen in (a), and with functional brain activation, as seen in (b), during grammaticality judgment. Residuals were calculated from a partial correlation analysis after controlling for age effects. See text for explanation.</p

Running time of the VN algorithm and Brandes' algorithm.

<p>(a) Networks with average degree and , 1% of the network edges are weighted with ; (b) Networks with average degree , all edges are weighted with .</p

Illustration of representing the weighted network (a) by an unweighted network with virtual nodes (b).

<p>Illustration of representing the weighted network (a) by an unweighted network with virtual nodes (b).</p