Biodegradable polyester-based nano drug delivery system in cancer chemotherapy: a review of recent progress (2021–2023)

Cancer presents a formidable threat to human health, with the majority of cases currently lacking a complete cure. Frequently, chemotherapy drugs are required to impede its progression. However, these drugs frequently suffer from drawbacks such as poor selectivity, limited water solubility, low bioavailability, and a propensity for causing organ toxicity. Consequently, a concerted effort has been made to seek improved drug delivery systems. Nano-drug delivery systems based on biodegradable polyesters have emerged as a subject of widespread interest in this pursuit. Extensive research has demonstrated their potential for offering high bioavailability, effective encapsulation, controlled release, and minimal toxicity. Notably, poly (ε-caprolactone) (PCL), poly (lactic-co-glycolic acid) (PLGA), and polylactic acid (PLA) have gained prominence as the most widely utilized options as carriers of the nano drug delivery system. This paper comprehensively reviews recent research on these materials as nano-carriers for delivering chemotherapeutic drugs, summarizing their latest advancements, acknowledging their limitations, and forecasting future research directions

Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize timedomain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of $4.53$ comparable to a MOS of $4.58$ for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the input to WaveNet instead of linguistic, duration, and $F_0$ features. We further demonstrate that using a compact acoustic intermediate representation enables significant simplification of the WaveNet architecture.Comment: Accepted to ICASSP 201

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.Comment: Submitted to Interspeech 2017. v2 changed paper title to be consistent with our conference submission (no content change other than typo fixes

Stratification of risk based on immune signatures and prediction of the efficacy of immune checkpoint inhibitors in prostate cancer

Prostate adenocarcinoma (PRAD) is a major threat to male health worldwide with a high mortality rate. New therapeutic strategies for the treatment of this malignant disease are of tremendous significance. Much attention has been paid to the involvement of immune cells in the prevention and treatment of cancer as well as how their regulatory systems contribute to effective cancer treatment. In this study, we constructed prognostic immune profiles based on The Cancer Genome Atlas (TCGA)-PRAD data sets and tested their predictive power on total and internal data sets. Then, we looked at how the lymphocyte of tumor invasion varied between the high-risk group and the low-risk group. Five immune-related genes made up the immune marker, which was an independent predictive factor in patients with PRAD. Patients in the low-risk score group had a higher rate of overall survival and a stronger infiltration of immune cells in the tumor microenvironment, which was highly related to clinical outcomes but required prospective validation

Machine Learning for Query Optimization

Data has been growing at an unprecedented rate in the past two decades. As a result, systems that store, process, and analyze data have become mission-critical. Crucial to the performance of data systems is the query optimizer, which translates high-level declarative queries (e.g., SQL) into efficient execution plans. However, query optimization is highly complex, leading to two key challenges. First, optimizers use a myriad of hand-designed heuristics to tame the complexity, but heuristics leave performance on the table. Second, optimizers are highly costly to develop, where human experts may spend months writing a first version and years refining it.This dissertation applies and enhances machine learning advances to tame the complexity in query optimization. First, we remove for the first time decades-old and accuracy-impacting heuristics in cardinality estimation—the Achilles’ heel of optimizers where heuristics particularly abound—thereby significantly improving estimation accuracy. We present Naru and NeuroCard, two cardinality estimators based on self-supervised learning advances that learn the joint data distribution of tables without any heuristic assumptions. Our estimators improve the accuracy of cardinality estimation by orders of magnitude compared to the prior state of the art. Second, we show that automatically learning to optimize SQL queries, without learning from an expert-designed optimizer, is both possible and efficient, thereby potentially alleviating the high development cost. We introduce Balsa, a deep reinforcement learning agent that automatically learns to optimize SQL queries by trial-and-error. Balsa can learn to outperform the optimizers of PostgreSQL—one of the most popular database systems—and a commercial database engine with a few hours of learning.Overall, by enhancing machine learning advances with new, carefully designed systems and ML techniques, this line of work improves existing query optimizers, while opening the possibility of alleviating the complex optimization in future environments and engines

Analysis of Block Orders and Its Implications for Market Participation Mode of Battery Energy Storage Stations

Flexible block orders designed by European electricity market is a relatively perfect time-sharing electricity price and clearing mechanism. It allows members to provide bidding form of diversification and individuation according to their own characteristics. This mechanism has features of flexibility, compatibility and expansibility, and can reasonably express the trading demands of various types of market participants, which including energy storage. Therefore, it can be used to solve the problem of market participation model of energy storage. Considering the actual situation in China, block orders are suitable for the daily, weekly and monthly markets to help battery energy storage stations. So that they can obtain low-price electric energy by participating in the medium and long-term electricity market and promote the development of them