Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper implements deep learning to predict one-month-ahead stock returns in the cross-section in the Japanese stock market and investigates the performance of the method. Our results show that deep neural networks generally outperform shallow neural networks, and the best networks also outperform representative machine learning models. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section.

Summary Mechanisms of prolonged cytopenia (PC) after chimeric antigen receptor (CAR) T‐cell therapy, an emerging therapy for relapsed or refractory diffuse large B‐cell lymphoma, remain elusive. Haematopoiesis is tightly regulated by the bone marrow (BM) microenvironment, called the ‘niche’. To investigate whether alterations in the BM niche cells are associated with PC, we analysed CD271+ stromal cells in BM biopsy specimens and the cytokine profiles of the BM and serum obtained before and on day 28 after CAR T‐cell infusion. Imaging analyses of the BM biopsy specimens revealed that CD271+ niche cells were severely impaired after CAR T‐cell infusion in patients with PC. Cytokine analyses after CAR T‐cell infusion showed that CXC chemokine ligand 12 and stem cell factor, niche factors essential for haematopoietic recovery, were significantly decreased in the BM of patients with PC, suggesting reduced niche cell function. The levels of inflammation‐related cytokines on day 28 after CAR T‐cell infusion were consistently high in the BM of patients with PC. Thus, we demonstrate for the first time that BM niche disruption and sustained elevation of inflammation‐related cytokines in the BM following CAR T‐cell infusion are associated with subsequent PC.

Motivation: Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs.Results: We developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5′-end processing and 3′-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain.Availability and Implementation: The source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502.Contact: yasu@bio.keio.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

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