With the wealth of medical image data, efficient curation is essential.
Assigning the sequence type to magnetic resonance images is necessary for
scientific studies and artificial intelligence-based analysis. However,
incomplete or missing metadata prevents effective automation. We therefore
propose a deep-learning method for classification of prostate cancer scanning
sequences based on a combination of image data and DICOM metadata. We
demonstrate superior results compared to metadata or image data alone, and make
our code publicly available at
this https URL

通过结合图像数据和 DICOM 元数据，我们提出了一种基于深度学习的前列腺癌扫描序列分类方法，相较于仅使用元数据或图像数据，我们展示了更优越的结果。

使用图像内容和元数据自动分类前列腺核磁共振系列类型

Automatic classification of prostate MR series type using image content  and metadata

Motivation: Curation of literature in life sciences is a growing challenge.
The continued increase in the rate of publication, coupled with the relatively
fixed number of curators worldwide presents a major challenge to developers of
biomedical knowledgebases. Very few knowledgebases have resources to scale to
the whole relevant literature and all have to prioritise their efforts.
Results: In this work, we take a first step to alleviating the lack of
curator time in RNA science by generating summaries of literature for
non-coding RNAs using large language models (LLMs). We demonstrate that
high-quality, factually accurate summaries with accurate references can be
automatically generated from the literature using a commercial LLM and a chain
of prompts and checks. Manual assessment was carried out for a subset of
summaries, with the majority being rated extremely high quality. We also
applied the most commonly used automated evaluation approaches, finding that
they do not correlate with human assessment. Finally, we apply our tool to a
selection of over 4,600 ncRNAs and make the generated summaries available via
the RNAcentral resource. We conclude that automated literature summarization is
feasible with the current generation of LLMs, provided careful prompting and
automated checking are applied.
Availability: Code used to produce these summaries can be found here:
this https URL and the dataset of contexts
and summaries can be found here:
this https URL Summaries are also
displayed on the RNA report pages in RNAcentral (this https URL)

通过使用大型语言模型（LLMs）和一系列提示和检查步骤，我们首次采取行动缓解 RNA 科学中缺乏编辑时间的问题，生成非编码 RNA 文献的摘要，并证明可以自动生成高质量、事实准确的摘要以及准确的参考文献。

LitSumm: 用于非编码 RNA 文献摘要的大型语言模型

LitSumm: Large language models for literature summarisation of  non-coding RNAs

Data are rapidly growing in size and importance for society, a trend
motivated by their enabling power. The accumulation of new data, sustained by
progress in technology, leads to a boundless expansion of stored data, in some
cases with an exponential increase in the accrual rate itself. Massive data are
hard to process, transmit, store, and exploit, and it is particularly hard to
keep abreast of the data store as a whole. This paper distinguishes three
phases in the life of data: acquisition, curation, and exploitation. Each
involves a distinct process, that may be separated from the others in time,
with a different set of priorities. The function of the second phase, curation,
is to maximize the future value of the data given limited storage. I argue that
this requires that (a) the data take the form of summary statistics and (b)
these statistics follow an endless process of rescaling. The summary may be
more compact than the original data, but its data structure is more complex and
it requires an on-going computational process that is much more sophisticated
than mere storage. Rescaling results in dimensionality reduction that may be
beneficial for learning, but that must be carefully controlled to preserve
relevance. Rescaling may be tuned based on feedback from usage, with the
proviso that our memory of the past serves the future, the needs of which are
not fully known.

本文探讨了数据从获取、筛选再到利用三个不同阶段的处理过程，强调了协调将数据变成摘要统计信息并不断调整缩放的重要性以最大化其价值。

优雅遗忘 II. 数据作为过程

Graceful Forgetting II. Data as a Process

Automating ontology construction and curation is an important but challenging
task in knowledge engineering and artificial intelligence. Prediction by
machine learning techniques such as contextual semantic embedding is a
promising direction, but the relevant research is still preliminary especially
for expressive ontologies in Web Ontology Language (OWL). In this paper, we
present a new subsumption prediction method named BERTSubs for classes of OWL
ontology. It exploits the pre-trained language model BERT to compute contextual
embeddings of a class, where customized templates are proposed to incorporate
the class context (e.g., neighbouring classes) and the logical existential
restriction. BERTSubs is able to predict multiple kinds of subsumers including
named classes from the same ontology or another ontology, and existential
restrictions from the same ontology. Extensive evaluation on five real-world
ontologies for three different subsumption tasks has shown the effectiveness of
the templates and that BERTSubs can dramatically outperform the baselines that
use (literal-aware) knowledge graph embeddings, non-contextual word embeddings
and the state-of-the-art OWL ontology embeddings.

本文介绍了一种基于 Bert 语言模型的子类预测方法 BERTSubs，能够有效地执行包括存在限制在内的多种子类预测任务，具有优于已有方法的性能。