Cluster diffusing shuffles

Unbiased shuffling algorithms, such as the Fisher-Yates shuffle, are often used for shuffle play in media players. These algorithms treat all items being shuffled equally regardless of how similar the items are to each other. While this may be desirable for many applications, this is problematic for shuffle play due to the clustering illusion, which is the tendency for humans to erroneously consider “streaks” or “clusters” that may arise from samplings of random distributions to be non-random. This thesis attempts to address this issue with a family of biased shuffling algorithms called cluster diffusing (CD) shuffles which are based on disordered hyperuniform systems such as the distribution of cone cells in chicken eyes, the energy levels of heavy atomic nuclei, the eigenvalue distributions of various types of random matrices, and many others which appear in a variety of biological, chemical, physical, and mathematical settings. These systems suppress density fluctuations at large length scales without appearing ordered like lattices, making them ideal for shuffle play. The CD shuffles range from a random matrix based shuffle which takes O(n^3) time and O(n^2) space to more efficient approximations which take O(n) time and O(n) space.Ope

Preparation of Stable Gold Colloids for Sensitivity Enhancement of Progesterone Immunoassay using Surface Plasmon Resonance

The purpose of this study was to prepare concentrated and stable gold colloids for the enhancement of the signal response of the SPR technique for detecting small molecules such as progesterone. The gold colloids developed in this study were prepared by hydrazine hydrate, sodium borohydride, and tri-potassium citrate reduction routes. The study revealed that the sodium borohydride reduced gold colloids were extremely stable and it was able to be utilised in the progesterone immunoassay developed previously by Mitchell et al. The experiment was carried out on BIAcore 3000 using two different sensor surfaces (CM5 and SAM). The results showed that the enhancement species prepared from the borohydride-reduced gold colloids were able to improve the SPR signal response by 13 times higher than SPR signal produced without the enhancement species on the CM5 surface. The signal enhancement on the SAM surface using the same enhancement species was even greater at 29 times higher. The sensitivity of the assay was, however, unable to be determined due to time constraint. The limit of detection (LOD) of the progesterone assay using the CM5 chip was estimated to be ca. 5-20 pg/mL. Whilst for the SAM chip, the LOD of the progesterone assay was estimated to be ca. 5-20 fg/mL. Further work is required to confirm these estimated LOD values

Solution Casting and Mechanical Testing of Arabinan-Cellulose Nanocomposites

The purpose of this work was to investigate methods to produce consistent, reliable, and testable thin films of arabinan-cellulose nanocomposites. Mechanical properties and composition of the Opuntia ficus-indica cactus spines served as motivation for this research. The high specific strength and stiffness, biodegradability, and sustainability of these spines inspired the creation of composites fabricated from the same materials found in cactus spines: arabinan and nanocrystalline cellulose (NCC). Arabinan serves as the matrix material and NCC as the reinforcement. To explore the feasibility of using a non-toxic solvent, different solution casting techniques with water as a solvent were investigated. Ultrasonication was used to disperse the NCC particles within an arabinan-water solution. A straightforward procedure using silicone molds yielded consistent samples that were suitable for tensile testing. SEM imaging showed signs of aggregation NCC particles. Composite stiffness, strength, and strain to break were found to be dependent on drying time, temperature, water content, and weight percent NCC. To obtain samples at similar water content, samples were monitored until any tacky spots on the film surface had completely dried. Composite samples with greater NCC content were found to have a higher strength and modulus compared to pure arabinan. Arabinan reinforced with 5 wt.% NCC had an average tensile strength of 7.66 MPa and stiffness of 309.03 MPa, while pure arabinan had 4.62 MPa and 211.37 MPa, respectively

The Future Self: Promoting Prosocial Decision-Making Through Motivated Episodic Simulation

Vividly imagining the future self can help inform our present decisions. Given that most attempts aimed at understanding the prosocial effect of imagining future episodes have focused on sensory properties, little is known about how prosocial motivations can explain the link between episodic simulation and helping intentions. Here, the current research investigated whether altruistically and reputationally motivated simulation of helping behavior promote a willingness to help a person in need. The study found that imagining helping episodes increased willingness to help relative to a control manipulation, especially when reputational concerns were made salient. Path modeling analyses revealed that the prosocial effect of motivated simulation was mediated by future self-continuity (i.e., the perceived connectedness to the future self). These results shed light on a previously unexplored mechanism underlying the relationship between episodic simulation and prosocial intentions. Implications for future research in prosocial behavior, future-oriented cognition, and moral self-concept is discussed

TRIP13 is a protein-remodeling AAA+ ATPase that catalyzes MAD2 conformation switching.

The AAA+ family ATPase TRIP13 is a key regulator of meiotic recombination and the spindle assembly checkpoint, acting on signaling proteins of the conserved HORMA domain family. Here we present the structure of the Caenorhabditis elegans TRIP13 ortholog PCH-2, revealing a new family of AAA+ ATPase protein remodelers. PCH-2 possesses a substrate-recognition domain related to those of the protein remodelers NSF and p97, while its overall hexameric architecture and likely structural mechanism bear close similarities to the bacterial protein unfoldase ClpX. We find that TRIP13, aided by the adapter protein p31(comet), converts the HORMA-family spindle checkpoint protein MAD2 from a signaling-active 'closed' conformer to an inactive 'open' conformer. We propose that TRIP13 and p31(comet) collaborate to inactivate the spindle assembly checkpoint through MAD2 conformational conversion and disassembly of mitotic checkpoint complexes. A parallel HORMA protein disassembly activity likely underlies TRIP13's critical regulatory functions in meiotic chromosome structure and recombination

Cell-type specific potent Wnt signaling blockade by bispecific antibody.

Cell signaling pathways are often shared between normal and diseased cells. How to achieve cell type-specific, potent inhibition of signaling pathways is a major challenge with implications for therapeutic development. Using the Wnt/β-catenin signaling pathway as a model system, we report here a novel and generally applicable method to achieve cell type-selective signaling blockade. We constructed a bispecific antibody targeting the Wnt co-receptor LRP6 (the effector antigen) and a cell type-associated antigen (the guide antigen) that provides the targeting specificity. We found that the bispecific antibody inhibits Wnt-induced reporter activities with over one hundred-fold enhancement in potency, and in a cell type-selective manner. Potency enhancement is dependent on the expression level of the guide antigen on the target cell surface and the apparent affinity of the anti-guide antibody. Both internalizing and non-internalizing guide antigens can be used, with internalizing bispecific antibody being able to block signaling by all ligands binding to the target receptor due to its removal from the cell surface. It is thus feasible to develop bispecific-based therapeutic strategies that potently and selectively inhibit signaling pathways in a cell type-selective manner, creating opportunity for therapeutic targeting

Multicamera 3D Viewpoint Adjustment for Robotic Surgery via Deep Reinforcement Learning

While robot-assisted minimally invasive surgery (RMIS) procedures afford a variety of benefits over open surgery and manual laparoscopic operations (including increased tool dexterity, reduced patient pain, incision size, trauma and recovery time, and lower infection rates [1], lack of spatial awareness remains an issue. Typical laparoscopic imaging can lack sufficient depth cues and haptic feedback, if provided, rarely reflects realistic tissue-tool interactions. This work is part of a larger ongoing research effort to reconstruct 3D surfaces using multiple viewpoints in RMIS to increase visual perception. The manual placement and adjustment of multicamera systems in RMIS are nonideal and prone to error [2], and other autonomous approaches focus on tool tracking and do not consider reconstruction of the surgical scene [3,4,5]. The group\u27s previous work investigated a novel, context-aware autonomous camera positioning method [6], which incorporated both tool location and scene coverage for multiple camera viewpoint adjustments. In this paper, the authors expand upon this prior work by implementing a streamlined deep reinforcement learning approach between optimal viewpoints calculated using the prior method [6] which encourages discovery of otherwise unobserved and additional camera viewpoints. Combining the framework and robustness of the previous work with the efficiency and additional viewpoints of the augmentations presented here results in improved performance and scene coverage promising towards real-time implementation

Supply network disruption and resilience: A network structural perspective

Increasingly, scholars recognize the importance of understanding supply network disruptions. However, the literature still lacks a clear conceptualization of a networkâ level understanding of supply disruptions. Not having a network level understanding of supply disruptions prevents firms from fully mitigating the negative effects of a supply disruption. Graph theory helps to conceptualize a supply network and differentiate between disruptions at the node/arc level vs. network level. The structure of a supply network consists of a collection of nodes (facilities) and the connecting arcs (transportation). From this perspective, small events that disrupt a node or arc in the network can have major consequences for the network. A failure in a node or arc can potentially stop the flow of material across network. This study conceptualizes supply network disruption and resilience by examining the structural relationships among entities in the network. We compare four fundamental supply network structures to help understand supply network disruption and resilience. The analysis shows that node/arcâ level disruptions do not necessarily lead to networkâ level disruptions, and demonstrates the importance of differentiating a node/arc disruption vs. a network disruption. The results also indicate that network structure significantly determines the likelihood of disruption. In general, different structural relationships among network entities have different levels of resilience. More specifically, resilience improves when the structural relationships in a network follow the powerâ law. This paper not only offers a new perspective of supply network disruption, but also suggests a useful analytical approach to assessing supply network structures for resilience.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146874/1/joom43.pd