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Successful cell division requires faithful division and segregation of organelles into daughter cells. The unicellular algaChlamydomonas reinhardtiihas a single, large chloroplast whose division is spatiotemporally coordinated with furrowing. Cytoskeletal structures form in the same plane at the midzone of the dividing chloroplast (FtsZ) and the cell (microtubules), but how these structures are coordinated is not understood. Previous work showed that loss of F-actin blocks chloroplast division but not furrow ingression, suggesting that pharmacological perturbations can disorganize these events. In this study, we developed an imaging platform to screen natural compounds that perturb cell division while monitoring FtsZ and microtubules and identified 70 unique compounds. One compound, curcumin, has been proposed to bind to both FtsZ and tubulin proteins in bacteria and eukaryotes, respectively. InC. reinhardtii,where both targets coexist and are involved in cell division, curcumin at a specific dose range caused a severe disruption of the FtsZ ring in chloroplast while leaving the furrow-associated microtubule structures largely intact. Time-lapse imaging showed that loss of FtsZ and chloroplast division failure delayed the completion of furrowing but not the initiation, suggesting that the chloroplast-division checkpoint proposed in other algae requires FtsZ or is absent altogether inC. reinhardtii.

Successful cell division requires the coordination of both organelle inheritance and cytokinesis. The unicellular algaChlamydomonas reinhardtii, which spatiotemporally coordinates the division of its chloroplast with cytokinesis, is an excellent model to study the regulation.

We screened libraries of natural compounds for perturbations of cell and/or chloroplast division, identifying 70 unique chemicals. By time-lapse microscopy using one of the hits, curcumin, we demonstrate that although chloroplast division failures delay the completion of cytokinesis, it does not impair initiation.

These findings suggest that the chloroplast-division checkpoint proposed in other algae requires FtsZ or is absent altogether inC. reinhardtii.

 

Gradual typing has emerged as a promising typing discipline for reconciling static and dynamic typing, which have respective strengths and shortcomings. Thanks to its promises, gradual typing has gained tremendous momentum in both industry and academia. A main challenge in gradual typing is that, however, the performance of its programs can often be unpredictable, and adding or removing the type of a a single parameter may lead to wild performance swings. Many approaches have been proposed to optimize gradual typing performance, but little work has been done to aid the understanding of the performance landscape of gradual typing and navigating the migration process (which adds type annotations to make programs more static) to avert performance slowdowns. Motivated by this situation, this work develops a machine-learning-based approach to predict the performance of each possible way of adding type annotations to a program. On top of that, many supports for program migrations could be developed, such as finding the most performant neighbor of any given configuration. Our approach gauges runtime overheads of dynamic type checks inserted by gradual typing and uses that information to train a machine learning model, which is used to predict the running time of gradual programs. We have evaluated our approach on 12 Python benchmarks for both guarded and transient semantics. For guarded semantics, our evaluation results indicate that with only 40 training instances generated from each benchmark, the predicted times for all other instances differ on average by 4% from the measured times. For transient semantics, the time difference ratio is higher but the time difference is often within 0.1 seconds. 

Gradual typing has emerged as a promising typing discipline for reconciling static and dynamic typing, which have respective strengths and shortcomings. Thanks to its promises, gradual typing has gained tremendous momentum in both industry and academia. A main challenge in gradual typing is that, however, the performance of its programs can often be unpredictable, and adding or removing the type of a a single parameter may lead to wild performance swings. Many approaches have been proposed to optimize gradual typing performance, but little work has been done to aid the understanding of the performance landscape of gradual typing and navigating the migration process (which adds type annotations to make programs more static) to avert performance slowdowns. Motivated by this situation, this work develops a machine-learning-based approach to predict the performance of each possible way of adding type annotations to a program. On top of that, many supports for program migrations could be developed, such as finding the most performant neighbor of any given configuration. Our approach gauges runtime overheads of dynamic type checks inserted by gradual typing and uses that information to train a machine learning model, which is used to predict the running time of gradual programs. We have evaluated our approach on 12 Python benchmarks for both guarded and transient semantics. For guarded semantics, our evaluation results indicate that with only 40 training instances generated from each benchmark, the predicted times for all other instances differ on average by 4% from the measured times. For transient semantics, the time difference ratio is higher but the time difference is often within 0.1 seconds. 

Gradual typing has emerged as a popular design point in programming languages, attracting significant interests from both academia and industry. Programmers in gradually typed languages are free to utilize static and dynamic typing as needed. To make such languages sound, runtime checks mediate the boundary of typed and untyped code. Unfortunately, such checks can incur significant runtime overhead on programs that heavily mix static and dynamic typing. To combat this overhead without necessitating changes to the underlying implementations of languages, we present discriminative typing. Discriminative typing works by optimistically inferring types for functions and implementing an optimized version of the function based on this type. To preserve safety it also implements an un-optimized version of the function based purely on the provided annotations. With two versions of each function in hand, discriminative typing translates programs so that the optimized functions are called as frequently as possible while also preserving program behaviors.

We have implemented discriminative typing in Reticulated Python and have evaluated its performance compared to guarded Reticulated Python. Our results show that discriminative typing improves the performance across 95% of tested programs, when compared to Reticulated, and achieves more than 4× speedup in more than 56% of these programs. We also compare its performance against a previous optimization approach and find that discriminative typing improved performance across 93% of tested programs, with 30% of these programs receiving speedups between 4 to 25 times. Finally, our evaluation shows that discriminative typing remarkably reduces the overhead of gradual typing on many mixed type configurations of programs.

In addition, we have implemented discriminative typing in Grift and evaluated its performance. Our evaluation demonstrations that DT significantly improves performance of Grift

 

Refinement types have been extensively used in class-based languages to specify and verify fine-grained logical specifications. Despite the advances in practical aspects such as applicability and usability, two fundamental issues persist. First, the soundness of existing class-based refinement type systems is inadequately explored, casting doubts on their reliability. Second, the expressiveness of existing systems is limited, restricting the depiction of semantic properties related to object-oriented constructs. This work tackles these issues through a systematic framework. We formalize a declarative class-based refinement type calculus (named RFJ), that is expressive and concise. We rigorously develop the soundness meta-theory of this calculus, followed by its mechanization in Coq. Finally, to ensure the calculus’s verifiability, we propose an algorithmic verification approach based on a fragment of first-order logic (named LFJ), and implement this approach as a type checker. 

The accumulation and transmission of mechanical stresses in the cell cortex and membrane determines the mechanics of cell shape and coordinates essential physical behaviors, from cell polarization to cell migration. However, the extent that the membrane and cytoskeleton each contribute to the transmission of mechanical stresses to coordinate diverse behaviors is unclear. Here, we reconstitute a minimal model of the actomyosin cortex within liposomes that adheres, spreads and ultimately ruptures on a surface. During spreading, accumulated adhesion-induced (passive) stresses within the membrane drive changes in the spatial assembly of actin. By contrast, during rupture, accumulated myosin-induced (active) stresses within the cortex determine the rate of pore opening. Thus, in the same system, devoid of biochemical regulation, the membrane and cortex can each play a passive or active role in the generation and transmission of mechanical stress, and their relative roles drive diverse biomimetic physical behaviors.

 

Abstract Computational modeling serves an important role in childbirth-related research. Prescribed fetal descent trajectory is a key characteristic in childbirth simulations. Two major types of fully prescribed fetal descent trajectories can be identified in the literature: straight descent trajectories and curve of Carus. The straight descent trajectory has the advantage of being simpler and can serve as a reasonable approximation for relatively small fetal movements during labor, but it cannot be used to simulate the entire childbirth process. The curve of Carus is the well-recognized fetal descent trajectory with physiological significance. However, no detailed procedure to geometrically define the curve of Carus can be found in existing computational studies. This status of curve of Carus simulation in the literature hinders the direct comparison of results across different studies and the advancement of computational techniques built upon previous research. The goals of this study are: (1) propose a universal approach to derive the curve of Carus for the second stage of labor, from the point when the fetal head engages the pelvis to the point when the fetal head is fully delivered; and (2) demonstrate its utility when considering various fetal head sizes. The current study provides a detailed formulation of the curve of Carus, considering geometries of both the mother and the fetus. The maternal geometries were obtained from MRI data, and the fetal head geometries were based on laser scanning of a replica of a real fetal head.