Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (GATE). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that GATE outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.Comment: 12+11 pages, 6+1 figures, 0+7 table

Grouping-matrix based Graph Pooling with Adaptive Number of Clusters

Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task which effectively captures the graph topology. Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assume that all input graphs share the same number of clusters. In inductive settings where the number of clusters can vary, however, the model should be able to represent this variation in its pooling layers in order to learn suitable clusters. Thus we propose GMPool, a novel differentiable graph pooling architecture that automatically determines the appropriate number of clusters based on the input data. The main intuition involves a grouping matrix defined as a quadratic form of the pooling operator, which induces use of binary classification probabilities of pairwise combinations of nodes. GMPool obtains the pooling operator by first computing the grouping matrix, then decomposing it. Extensive evaluations on molecular property prediction tasks demonstrate that our method outperforms conventional methods.Comment: 10 pages, 3 figure

3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation

Pretraining molecular representations from large unlabeled data is essential for molecular property prediction due to the high cost of obtaining ground-truth labels. While there exist various 2D graph-based molecular pretraining approaches, these methods struggle to show statistically significant gains in predictive performance. Recent work have thus instead proposed 3D conformer-based pretraining under the task of denoising, which led to promising results. During downstream finetuning, however, models trained with 3D conformers require accurate atom-coordinates of previously unseen molecules, which are computationally expensive to acquire at scale. In light of this limitation, we propose D&D, a self-supervised molecular representation learning framework that pretrains a 2D graph encoder by distilling representations from a 3D denoiser. With denoising followed by cross-modal knowledge distillation, our approach enjoys use of knowledge obtained from denoising as well as painless application to downstream tasks with no access to accurate conformers. Experiments on real-world molecular property prediction datasets show that the graph encoder trained via D&D can infer 3D information based on the 2D graph and shows superior performance and label-efficiency against other baselines.Comment: 16 pages, 5 figure

Effect of few-walled carbon nanotube crystallinity on electron field emission property

We discuss the influence of few-walled carbon nanotubes (FWCNTs) treated with nitric acid and/or sulfuric acid on field emission characteristics. FWCNTs/tetraethyl orthosilicate (TEOS) thin film field emitters were fabricated by a spray method using FWCNTs/TEOS sol one-component solution onto indium tin oxide (ITO) glass. After thermal curing, they were found tightly adhered to the ITO glass, and after an activation process by a taping method, numerous FWCNTs were aligned preferentially in the vertical direction. Pristine FWCNT/ TEOS-based field emitters revealed higher current density, lower turn-on field, and a higher field enhancement factor than the oxidized FWCNTs-based field emitters. However, the unstable dispersion of pristine FWCNT in TEOS/N,N-dimethylformamide solution was not applicable to the field emitter fabrication using a spray method. Although the field emitter of nitric acid-treated FWCNT showed slightly lower field emission characteristics, this could be improved by the introduction of metal nanoparticles or resistive layer coating. Thus, we can conclude that our spray method using nitric acid-treated FWCNT could be useful for fabricating a field emitter and offers several advantages compared to previously reported techniques such as chemical vapor deposition and screen printing.ope

Antitumor activity of sorafenib-incorporated nanoparticles of dextran/poly(dl-lactide-co-glycolide) block copolymer

Sorafenib-incoporated nanoparticles were prepared using a block copolymer that is composed of dextran and poly(DL-lactide-co-glycolide) [DexbLG] for antitumor drug delivery. Sorafenib-incorporated nanoparticles were prepared by a nanoprecipitation-dialysis method. Sorafenib-incorporated DexbLG nanoparticles were uniformly distributed in an aqueous solution regardless of the content of sorafenib. Transmission electron microscopy of the sorafenib-incorporated DexbLG nanoparticles revealed a spherical shape with a diameter < 300 nm. Sorafenib-incorporated DexbLG nanoparticles at a polymer/drug weight ratio of 40:5 showed a relatively uniform size and morphology. Higher initial drug feeding was associated with increased drug content in nanoparticles and in nanoparticle size. A drug release study revealed a decreased drug release rate with increasing drug content. In an in vitro anti-proliferation assay using human cholangiocarcinoma cells, sorafenib-incorporated DexbLG nanoparticles showed a similar antitumor activity as sorafenib. Sorafenib-incorporated DexbLG nanoparticles are promising candidates as vehicles for antitumor drug targeting

Utility of targeted deep sequencing for detecting circulating tumor DNA in pancreatic cancer patients.

Targeted deep sequencing across broad genomic regions has been used to detect circulating tumor DNA (ctDNA) in pancreatic ductal adenocarcinoma (PDAC) patients. However, since most PDACs harbor a mutation in KRAS, sequencing of broad regions needs to be systemically compared to analyzing only KRAS mutations for PDAC. Using capture-based targeted deep sequencing, we detected somatic tumor mutations in 17 fine needle aspiration biopsy and 69 longitudinal cell-free DNA (cfDNA) samples from 17 PDAC patients. KRAS mutations were detected in 10 out of 17 pretreatment patient plasma samples. Next, interrogation of genetic alterations in matched primary tumor samples detected ctDNA in 12 of 17 pretreatment plasma samples and cfDNA sequencing across the 83 target genes identified ctDNA in 15 of 17 cases (88.2% sensitivity). This improved sensitivity of ctDNA detection resulted in enhanced tumor burden monitoring when we analyzed longitudinal plasma samples. We found that cfDNA sequencing detected the lowest mutant allelic fractions and number of variants when complete response or partial response to chemotherapy was achieved. We demonstrated that ctDNA levels measured by targeted deep sequencing sensitively indicate the presence of cancer and correlate well with clinical responses to therapy and disease progression in PDAC patients

AKARI Detection of the Infrared-Bright Supernova Remnant B0104-72.3 in the Small Magellanic Cloud

We present a serendipitous detection of the infrared-bright supernova remnant (SNR) B0104-72.3 in the Small Magellanic Cloud by the Infrared Camera (IRC) onboard AKARI. An elongated, partially complete shell is detected in all four observed IRC bands covering 2.6-15 um. The infrared shell surrounds radio, optical, and X-ray emission associated with the SNR and is probably a radiative SNR shell. This is the first detection of a SNR shell in this near/mid-infrared waveband in the Small Magellanic Cloud. The IRC color indicates that the infrared emission might be from shocked H2 molecules with some possible contributions from ionic lines. We conclude that B0104-72.3 is a middle-aged SNR interacting with molecular clouds, similar to the Galactic SNR IC 443. Our results highlight the potential of AKARI IRC observations in studying SNRs, especially for diagnosing SNR shocks.Comment: 12 pages with 3 figures, accepted for publication in AKARI PASJ special issu