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  1. Kidney cancer is a kind of high mortality cancer because of the difficulty in early diagnosis and the high metastatic dissemination in treatments. The surgical resection of tumors is the most effective treatment for renal cancer patients. However, precise assessment of tumor margins is a challenge during surgical resection. The objective of this study is to demonstrate an optical imaging tool in precisely distinguishing kidney tumor borders and identifying tumor zones from normal tissues to assist surgeons in accurately resecting tumors from kidneys during the surgery. 30 samples from six human kidneys were imaged using polarization-sensitive optical coherence tomography (PS-OCT). Cross-sectional, enface, and spatial information of kidney samples were obtained for microenvironment reconstruction. Polarization parameters (phase retardation, optic axis direction, and degree of polarization uniformity (DOPU) and Stokes parameters (Q, U, and V) were utilized for multiparameter analysis. To verify the detection accuracy of PS-OCT, H&E histology staining and dice-coefficient were utilized to quantify the performance of PS-OCT in identifying tumor borders and regions. In this study, tumor borders were clearly identified by PS-OCT imaging, which outperformed the conventional intensity-based OCT. With H&E histological staining as golden standard, PS-OCT precisely identified the tumor regions and tissue distributions at different locations and different depths based on polarization and Stokes parameters. Compared to the traditional attenuation coefficient quantification method, PS-OCT demonstrated enhanced contrast of tissue characteristics between normal and cancerous tissues due to the birefringence effects. Our results demonstrated that PS-OCT was promising to provide imaging guidance for the surgical resection of kidney tumors and had the potential to be used for other human kidney surgeries in clinics such as renal biopsy. 
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    Free, publicly-accessible full text available February 1, 2025
  2. Cloud systems are increasingly being managed by operation programs termed operators, which automate tedious, human-based operations. Operators of modern management platforms like Kubernetes, Twine, and ECS implement declarative interfaces based on the state-reconciliation principle. An operation declares a desired system state and the operator automatically reconciles the system to that declared state. Operator correctness is critical, given the impacts on system operations—bugs in operator code put systems in undesired or error states, with severe consequences. However, validating operator correctness is challenging due to the enormous system-state space and complex operation interface. A correct operator must not only satisfy correctness properties of its own code, but it must also maintain managed systems in desired states. Unfortunately, end-to-end testing of operators significantly falls short. We present Acto, the first automatic end-to-end testing technique for cloud system operators. Acto uses a statecentric approach to test an operator together with a managed system. Acto continuously instructs an operator to reconcile a system to different states and checks if the system successfully reaches those desired states. Acto models operations as state transitions and systematically realizes state-transition sequences to exercise supported operations in different scenarios. Acto’s oracles automatically check whether a system’s state is as desired. To date, Acto has helped find 56 serious new bugs (42 were confirmed and 30 have been fixed) in eleven Kubernetes operators with few false alarms. 
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    Free, publicly-accessible full text available October 23, 2024
  3. Free, publicly-accessible full text available September 4, 2024
  4. Free, publicly-accessible full text available July 23, 2024
  5. Abstract

    Epidural anesthesia helps manage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization‐sensitive optical coherence tomography (PS‐OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS‐OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross‐testing accuracy of 91.53%. These results showed the improved precision by PS‐OCT in guiding epidural anesthesia needle placement.

     
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    Free, publicly-accessible full text available October 25, 2024
  6. Abstract Summary

    In any population under selective pressure, a central challenge is to distinguish the genes that drive adaptation from others which, subject to population variation, harbor many neutral mutations de novo. We recently showed that such genes could be identified by supplementing information on mutational frequency with an evolutionary analysis of the likely functional impact of coding variants. This approach improved the discovery of driver genes in both lab-evolved and environmental Escherichia coli strains. To facilitate general adoption, we now developed ShinyBioHEAT, an R Shiny web-based application that enables identification of phenotype driving gene in two commonly used model bacteria, E.coli and Bacillus subtilis, with no specific computational skill requirements. ShinyBioHEAT not only supports transparent and interactive analysis of lab evolution data in E.coli and B.subtilis, but it also creates dynamic visualizations of mutational impact on protein structures, which add orthogonal checks on predicted drivers.

    Availability and implementation

    Code for ShinyBioHEAT is available at https://github.com/LichtargeLab/ShinyBioHEAT. The Shiny application is additionally hosted at http://bioheat.lichtargelab.org/.

     
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  7. Epoxy-based polymer networks from step-growth polymerizations are ubiquitous in coatings, adhesives, and as matrices in composite materials. Dynamic covalent bonds in the network allow its degradation into small molecules and thus, enable chemical recycling; however, such degradation often requires elevated temperatures and costly chemicals, resulting in various small molecules. Here, we design crosslinked polyesters from structurally similar epoxy and anhydride monomers derived from phthalic acid. We achieve selective degradation of the polyesters through transesterification reactions at near-ambient conditions using an alkali carbonate catalyst, resulting in a singular phthalic ester. We also demonstrate upcycling the network polyesters to photopolymers by one-step depolymerization using a functional alcohol. 
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    Free, publicly-accessible full text available June 27, 2024
  8. Clouded leopards (Neofelisspp.), a morphologically and ecologically distinct lineage of big cats, are severely threatened by habitat loss and fragmentation, targeted hunting, and other human activities. The long-held poor understanding of their genetics and evolution has undermined the effectiveness of conservation actions. Here, we report a comprehensive investigation of the whole genomes, population genetics, and adaptive evolution ofNeofelis. Our results indicate the genusNeofelisarose during the Pleistocene, coinciding with glacial-induced climate changes to the distributions of savannas and rainforests, and signatures of natural selection associated with genes functioning in tooth, pigmentation, and tail development, associated with clouded leopards’ unique adaptations. Our study highlights high-altitude adaptation as the main factor driving nontaxonomic population differentiation inNeofelis nebulosa. Population declines and inbreeding have led to reduced genetic diversity and the accumulation of deleterious variation that likely affect reproduction of clouded leopards, highlighting the urgent need for effective conservation efforts.

     
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    Free, publicly-accessible full text available October 6, 2024
  9. Workload autoscaling is widely used in public and private cloud systems to maintain stable service performance and save resources. However, it remains challenging to set the optimal resource limits and dynamically scale each workload at runtime. Reinforcement learning (RL) has recently been proposed and applied in various systems tasks, including resource management. In this paper, we first characterize the state-of-the-art RL approaches for workload autoscaling in a public cloud and point out that there is still a large gap in taking the RL advances to production systems. We then propose AWARE, an extensible framework for deploying and managing RL-based agents in production systems. AWARE leverages meta-learning and bootstrapping to (a) automatically and quickly adapt to different workloads, and (b) provide safe and robust RL exploration. AWARE provides a common OpenAI Gym-like RL interface to agent developers for easy integration with different systems tasks. We illustrate the use of AWARE in the case of workload autoscaling. Our experiments show that AWARE adapts a learned autoscaling policy to new workloads 5.5x faster than the existing transfer-learning-based approach and provides stable online policy-serving performance with less than 3.6% reward degradation. With bootstrapping, AWARE helps achieve 47.5% and 39.2% higher CPU and memory utilization while reducing SLO violations by a factor of 16.9x during policy training. 
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    Free, publicly-accessible full text available July 1, 2024