Fluorescence-based quantification of messenger RNA and plasmid DNA decay kinetics in extracellular biological fluids and cell extracts

Extracellular and intracellular degradation of nucleic acids remains an issue in non-viral gene therapy. Understanding biodegradation is critical for the rational design of gene therapeutics in order to maintain stability and functionality at the target site. However, there are only limited methods available that allow determining the stability of genetic materials in biological environments. In this context, the decay kinetics of fluorescently labeled plasmid DNA (pDNA) and messenger RNA (mRNA) in undiluted biological samples (i.e., human serum, human ascites, bovine vitreous) and cell extracts is studied using fluorescence correlation spectroscopy (FCS) and single particle tracking (SPT). It is demonstrated that FCS is suitable to follow mRNA degradation, while SPT is better suited to investigate pDNA integrity. The half-life of mRNA and pDNA is approximate to 1-2 min and 1-4 h in biological samples, respectively. The resistance against biodegradation drastically improves by complexation with lipid-based carriers. Taken together, FCS and SPT are able to quantify the integrity of mRNA and pDNA, respectively, as a function of time, both in the extracellular biological fluids and cell extracts. This in turn allows to focus on the important but less understood issue of nucleic acids degradation in more detail and to rationally optimize gene delivery system as therapeutics

Branch Lengths on Birth-Death Trees and the Expected Loss of Phylogenetic Diversity

Diversification is nested, and early models suggested this could lead to a great deal of evolutionary redundancy in the Tree of Life. This result is based on a particular set of branch lengths produced by the common coalescent, where pendant branches leading to tips can be very short compared with branches deeper in the tree. Here, we analyze alternative and more realistic Yule and birth-death models. We show how censoring at the present both makes average branches one half what we might expect and makes pendant and interior branches roughly equal in length. Although dependent on whether we condition on the size of the tree, its age, or both, these results hold both for the Yule model and for birth-death models with moderate extinction. Importantly, the rough equivalency in interior and exterior branch lengths means that the loss of evolutionary history with loss of species can be roughly linear. Under these models, the Tree of Life may offer limited redundancy in the face of ongoing species los

Classification of small renal masses based on CT images and machine learning algorithms

Kidney tumor is among the leading causes of tumors and deaths worldwide. In all kidney tumor cases, an increasing number of small renal masses (SRMs) with a size smaller than 4 cm have been detected and they are becoming a typical problem for radiologists and surgeons. Most SRMs are either of renal angiomyolipoma (AML) or renal cell carcinoma (RCC), the former being benign and the latter being malignant. The malignant ones can be further classified into three types, clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (chRCC). Different kind of renal tumor requires varied treatment and management. In recent years, four-phase computer tomography (CT) has become the standard approach for kidney tumor examination. In most circumstances, classic AMLs and RCCs can be classified by a radiologist reading the CT images. While fat poor angiomyolipomas (fp-AML) set barriers to this classification method due to the loss of typical diagnosis characteristics. Radiologists are also incapable of differentiating malignant tumors. For now, SRM classification is mainly performed by pathological examination, which is time and resource consuming. Machine learning and one of its branch, deep learning, has been extended to medical image processing field. In this paper, support vector machine (SVM) and convolutional neural network (CNN) were respectively used to build models with the input of one of the last three phases of CT images and the combination of them. For the establishment of each model, at least 20% of overall patient cases were picked out randomly as independent testing subset and the rest undertook 10-fold cross validation for an objective and reliable evaluation of the models. It turned out that SVM algorithm using a linear kernel with phase 2 (corticomedullary) images as input acquired an accuracy of 0.93 and a sensitivity of 0.97 on patient’s tumor type prediction of fp-AML/RCC classification. CNN algorithm, consisting of 12 layers including 4 convolutional layers each followed by a max-pooling layer, one flatten layer, and three densely connected layers, with the help of activation functions, dropout strategy, and stochastic gradient descent (SGD) optimization method, achieved an accuracy of 0.85 on pRCC/chRCC/ccRCC categorization with phase 2 images as input. Images of corticomedullary stage were proved to be eligible for classifiers. This can be seen as a breakthrough since it is the first successful application of deep learning networks in renal tumor classification. Meanwhile, these two models were both balanced over different classes and they together provide a comprehensive solution to SRM classification. Given these findings, the two models can be a preliminary step for machine learning and especially deep learning algorithms to assist, improve, and finally revolutionize the conventional clinical decision making process to guide appropriate management and treatment

Scalable and Efficient Machine Learning as a Service

Driven by the sustained advances of machine learning and its application to multiple domains ranging from image recognition, text prediction to translation and autonomous driving, the past few years have witnessed a surging demand for Machine-Learning-as-a-Service (MLaaS). MLaaS is an emerging computing paradigm that facilitates machine learning model design, model training, inference serving and provides optimized executions of machine learning tasks in an automated, scalable, and efficient manner.This dissertation proposes three novel approaches for MLaaS, namely SimiGrad, DistQuant, and RRL, to improve the scale and efficiency of MLaaS training and inference, respectively.For MLaaS training, we propose SimiGrad, a fine-grained adaptive batching approach for large scale training using gradient similarity measurement. Large scale training requires massive parallelism to finish the training within a reasonable amount of time. To support massive parallelism, large batch training is the key enabler but often at the cost of generalization performance. We propose a fully automated and lightweight adaptive batching methodology to enable fine-grained batch size adaption (e.g., at a mini-batch level) that can achieve state-of-the-art performance with record breaking batch sizes. The core component of our method is a lightweight yet efficient representation of the critical gradient noise information. We open-source the proposed methodology and extensive evaluations on popular benchmarks (e.g., CIFAR10, ImageNet, and BERT-Large) demonstrate that the proposed methodology outperforms state-of-the-art methodologies using adaptive batching approaches or hand-tuned static strategies in both performance and batch size. Particularly, we achieve a new state-of-the-art batch size of 78K in BERT-Large pretraining with a SQuAD score of 90.69 compared to 90.58 reported in previous state-of-the-art with 59K batch size.Another key challenge for MLaaS training is the communication cost which limits how much the training can scale. Quantization is a popular method for reducing communication cost yet it imposes non-trivial encoding and decoding overheads and may lead to degraded model performance. Our key observation is that model weights are partitioned and cached in GPU memory in common distributed training methods such as model and pipeline parallelism. If quantization can be performed on the partitioned weights in parallel while cached in GPU memory, the quantization speed can be significantly improved and we can further reduce the communication overhead for weights gathering. To this end, we propose DistQuant, a distributed quantization scheme for compressing partitioned weights during distributed training. DistQuant preserves model performance by canceling out the noise introduced by quantization and is transparent to training pipelines. We both theoretically and empirically show that DistQuant can achieve much higher precision than state-of-the-art quantization approaches. Evaluation on large-scale models including BERT and GPT2 indicates that DistQuant reduces the communication cost of MLaaS training by half without compromising model performance.For MLaaS serving, we propose RRL, a swift machine learning model serving system powered by a region-based reinforcement learning approach. To meet latency Service-Level-Objective (SLO), judicious parallelization at both request and operation levels is utterly important. However, existing ML systems (e.g., Tensorflow) and cloud ML serving platforms (e.g., SageMaker) are SLO-agnostic and rely on users to manually configure the parallelism. To provide low latency MLaaS serving, we propose a swift machine learning serving scheduling framework with a novel Region-based Reinforcement Learning (RRL) approach. RRL can efficiently identify the optimal parallelism configuration under different workloads by estimating performance of similar configurations with that of the known ones. We both theoretically and experimentally show that the RRL approach can outperform state-of-the-art approaches by finding near-optimal solutions over 8 times faster while reducing inference latency up to 79.0% and reducing SLO violation up to 49.9%

Answering Layer 3 queries with DiscoSCMs

In the realm of causal inference, the primary frameworks are the Potential Outcome (PO) and the Structural Causal Model (SCM), both predicated on the consistency rule. However, when facing Layer 3 valuations, i.e., counterfactual queries that inherently belong to individual-level semantics, they both seem inadequate due to the issue of degeneration caused by the consistency rule. For instance, in personalized incentive scenarios within the internet industry, the probability of one particular user being a complier, denoted as $P(y_x, y'_{x'})$, degenerates to a parameter that can only take values of 0 or 1. This paper leverages the DiscoSCM framework to theoretically tackle the aforementioned counterfactual degeneration problem, which is a novel framework for causal modeling that combines the strengths of both PO and SCM, and could be seen as an extension of them. The paper starts with a brief introduction to the background of causal modeling frameworks. It then illustrates, through an example, the difficulty in recovering counterfactual parameters from data without imposing strong assumptions. Following this, we propose the DiscoSCM with independent potential noise framework to address this problem. Subsequently, the superior performance of the DiscoSCM framework in answering counterfactual questions is demonstrated by several key results in the topic of unit select problems. We then elucidate that this superiority stems from the philosophy of individual causality. In conclusion, we suggest that DiscoSCM may serve as a significant milestone in the causal modeling field for addressing counterfactual queries

Does China's overseas lending favor One Belt One Road countries?

The One Belt One Road initiative is found to promote China’s overseas lending in the belt road countries, especially for countries along the continental route. Such effect strengthens and persists for at least three years. Our findings show that launching a national strategy could be a decisive determinant of one country’s outbound loans