Clinical characteristics of cognitive deficits in major depressive disorder: a 6-month prospective study

Background: Previous studies have shown that major depressive disorder (MDD) is associated with a variety of cognitive deficits, which can persist even in remitted states. Nevertheless, the relationship between the cognitive and affective symptoms in depression remains obscure. The aim of the present study was to explore the clinical characteristics and correlates of the cognitive deficits in patients with MDD. Methods: Clinical and neuropsychological assessments were conducted at baseline and 6-month follow-ups. The severity of the disease and the effect of treatment were assessed with the Hamilton Depression Scale-17. Neuropsychological tests, including the digital symbol substitution test and digit span test, were administered to 67 depressed patients and 56 healthy participants. Results: MDD patients showed impairments in memory, attention, and executive function at baseline. After the 6-month treatment phase, patients in remission showed significant alleviation of these cognitive deficits, although impairments in attention and executive function were still present when compared to controls. Discussion: Significant cognitive deficits are present in MDD. The speed of remission of cognitive functions seems to be slower than and inconsistent with emotional symptoms, which provides new support for the argument that cognitive deficits are independent factors from the emotional symptoms in MDD

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions

The sequence-to-sequence (Seq2Seq) approach has recently been widely used in grammatical error correction (GEC) and shows promising performance. However, the Seq2Seq GEC approach still suffers from two issues. First, a Seq2Seq GEC model can only be trained on parallel data, which, in GEC task, is often noisy and limited in quantity. Second, the decoder of a Seq2Seq GEC model lacks an explicit awareness of the correctness of the token being generated. In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token. We discover and investigate two types of critics: a pre-trained left-to-right language model critic and an incremental target-side grammatical error detector critic. Through extensive experiments on English and Chinese datasets, our framework consistently outperforms strong baselines and achieves results competitive with state-of-the-art methods.Comment: Accept to Findings of EMNLP 202

Patients\u27 Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Les quatre textes ont en commun de présenter certaines évolutions récentes de l’histoire politique en Allemagne. Ils prennent tous position face à trois tournants historiographiques. La notion d’histoire culturelle du politique peut servir d’emblème au premier de ces tournants : le politique est envisagé non plus comme une succession d’événements ni comme le fruit de déterminations structurelles dont il serait la superstructure ou l’écume, mais comme l’expression de valeurs et de procédures o..

The role of N6-methyladenosine (m6A) in kidney diseases

Chemical modifications are a specific and efficient way to regulate the function of biological macromolecules. Among them, RNA molecules exhibit a variety of modifications that play important regulatory roles in various biological processes. More than 170 modifications have been identified in RNA molecules, among which the most common internal modifications include N6-methyladenine (m6A), n1-methyladenosine (m1A), 5-methylcytosine (m5C), and 7-methylguanine nucleotide (m7G). The most widely affected RNA modification is m6A, whose writers, readers, and erasers all have regulatory effects on RNA localization, splicing, translation, and degradation. These functions, in turn, affect RNA functionality and disease development. RNA modifications, especially m6A, play a unique role in renal cell carcinoma disease. In this manuscript, we will focus on the biological roles of m6A in renal diseases such as acute kidney injury, chronic kidney disease, lupus nephritis, diabetic kidney disease, and renal cancer

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Product BMO, Little BMO, and Riesz Commutators in the Bessel Setting

In this paper, we study the product BMO space, little bmo space, and their connections with the corresponding commutators associated with Bessel operators studied by Weinstein, Huber, and Muckenhoupt–Stein. We first prove that the product BMO space in the Bessel setting can be used to deduce the boundedness of the iterated commutators with the Bessel Riesz transforms. We next study the little bmo space in this Bessel setting and obtain the equivalent characterization of this space in terms of commutators, where the main tool that we develop is the characterization of the predual of little bmo and its weak factorizations. We further show that in analogy with the classical setting the little bmo space is a proper subspace of the product BMO space. These extend the previous related results studied by Cotlar–Sadosky and Ferguson–Sadosky on the bidisc to the Bessel setting, where the usual analyticity and Fourier transform do not apply

Product BMO, Little BMO, and Riesz Commutators in the Bessel Setting

In this paper, we study the product BMO space, little bmo space, and their connections with the corresponding commutators associated with Bessel operators studied by Weinstein, Huber, and Muckenhoupt–Stein. We first prove that the product BMO space in the Bessel setting can be used to deduce the boundedness of the iterated commutators with the Bessel Riesz transforms. We next study the little bmo space in this Bessel setting and obtain the equivalent characterization of this space in terms of commutators, where the main tool that we develop is the characterization of the predual of little bmo and its weak factorizations. We further show that in analogy with the classical setting the little bmo space is a proper subspace of the product BMO space. These extend the previous related results studied by Cotlar–Sadosky and Ferguson–Sadosky on the bidisc to the Bessel setting, where the usual analyticity and Fourier transform do not apply

A Comparison of Machine Learning and Empirical Approaches for Deriving Bathymetry from Multispectral Imagery

Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data