Early phases of different types of isolated neutron star

Two Galactic isolated strong X-ray pulsars seem to be in the densest environments compared to other types of Galactic pulsar. X-ray pulsar J1846-0258 can be in an early phase of anomalous X-ray pulsars and soft gamma repeaters if its average braking index is ~1.8-2.0. X-ray pulsar J1811-1925 must have a very large average braking index (n~11) if this pulsar was formed by SN 386AD. This X-ray pulsar can be in an early phase of evolution of the radio pulsars located in the region P~50-150 ms and \.{P}~10$^{-14}-10^{-16}$ s/s of the P-\.{P} diagram. X-ray/radio pulsar J0540-69 seems to be evolving in the direction to the dim isolated thermal neutron star region on the P-\.{P} diagram. Possible progenitors of different types of neutron star are also discussed.Comment: to appear in the International Journal of Modern Physics

Exploiting Pretrained Biochemical Language Models for Targeted Drug Design

Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target specific training. We also compare two decoding strategies to generate compounds: beam search and sampling. Results: The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality. Availability and implementation: The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145Comment: 12 pages, to appear in Bioinformatic

Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words (analogous to the words that make up sentences in human languages) and then apply advanced natural language processing techniques for tasks such as $\textit{de novo}$ drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. This study aims to investigate the chemical vocabularies generated by popular subword tokenization algorithms, namely Byte Pair Encoding (BPE), WordPiece, and Unigram, and identify key chemical words associated with protein-ligand binding. To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. Further, we conduct case studies on a number of protein families to analyze the impact of key chemical words on binding. Through our analysis, we find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our findings will help shed light on the chemistry captured by the chemical words, and by machine learning models for drug discovery at large.Comment: 16 pages, 11 figures, new computational analysis and extended case studie

Nano-bioceramic synthesis from tropical sea snail shells (Tiger Cowrie - Cypraea Tigris) with simple chemical treatment

In this study several bioceramic materials (i.e. hydroxyapatite, whitlockite) were prepared by using chemical synthesis method from sea snail shells (Tiger Cowrie - Cypraea Tigris), originated from Pacific Ocean. Marine shells usually present aragonite-calcite structures and generally, complicated and pressurized equipment is necessary to convert these structures into bioceramics. Instead of using complicated systems, a basic ultrasonic equipment and simple chemical synthesis method was used in the process. DTA analysis was performed to calculate the required amount of H3PO4 solution in order to set the appropriate stoichiometric ratio of Ca/P equal to 1.667 for HA bioceramic or to 1.5 for β-TCP bioceramic in the titration. The prepared batches were sintered at 800°C and 400 °C for hydroxyapatite (HA) and β-tri calcium phosphate (β-TCP) forms respectively. X-ray diffraction analysis, scanning electron microscopy (SEM) and infrared observations (FTIR) were implemented for both TCP and HA bioceramics. By applying the chemical synthesis with basic ultrasonic equipment, this study proposes a simple way of production for nano-HA/TCP powders from a natural marine sources

A Twisted Kink Crystal in the Chiral Gross-Neveu model

We present the detailed properties of a self-consistent crystalline chiral condensate in the massless chiral Gross-Neveu model. We show that a suitable ansatz for the Gorkov resolvent reduces the functional gap equation, for the inhomogeneous condensate, to a nonlinear Schr\"odinger equation, which is exactly soluble. The general crystalline solution includes as special cases all previously known real and complex condensate solutions to the gap equation. Furthermore, the associated Bogoliubov-de Gennes equation is also soluble with this inhomogeneous chiral condensate, and the exact spectral properties are derived. We find an all-orders expansion of the Ginzburg-Landau effective Lagrangian and show how the gap equation is solved order-by-order.Comment: 28 pages, 13 figs; v2: new appendix on Eilenberger eq and refs; version in PR

Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.This work was supported by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Nature Publishing Group

Global respiratory syncytial virus–related infant community deaths

Background Respiratory syncytial virus (RSV) is a leading cause of pediatric death, with >99% of mortality occurring in low- and lower middle-income countries. At least half of RSV-related deaths are estimated to occur in the community, but clinical characteristics of this group of children remain poorly characterized. Methods The RSV Global Online Mortality Database (RSV GOLD), a global registry of under-5 children who have died with RSV-related illness, describes clinical characteristics of children dying of RSV through global data sharing. RSV GOLD acts as a collaborative platform for global deaths, including community mortality studies described in this supplement. We aimed to compare the age distribution of infant deaths <6 months occurring in the community with in-hospital. Results We studied 829 RSV-related deaths <1 year of age from 38 developing countries, including 166 community deaths from 12 countries. There were 629 deaths that occurred <6 months, of which 156 (25%) occurred in the community. Among infants who died before 6 months of age, median age at death in the community (1.5 months; IQR: 0.8−3.3) was lower than in-hospital (2.4 months; IQR: 1.5−4.0; P < .0001). The proportion of neonatal deaths was higher in the community (29%, 46/156) than in-hospital (12%, 57/473, P < 0.0001). Conclusions We observed that children in the community die at a younger age. We expect that maternal vaccination or immunoprophylaxis against RSV will have a larger impact on RSV-related mortality in the community than in-hospital. This case series of RSV-related community deaths, made possible through global data sharing, allowed us to assess the potential impact of future RSV vaccines