The dengue-specific immune response and antibody identification with machine learning.

Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.

Heavy-element production in a compact object merger observed by JWST.

The mergers of binary compact objects such as neutron stars and black holes are of central interest to several areas of astrophysics, including as the progenitors of gamma-ray bursts (GRBs)1, sources of high-frequency gravitational waves (GWs)2 and likely production sites for heavy-element nucleosynthesis by means of rapid neutron capture (the r-process)3. Here we present observations of the exceptionally bright GRB 230307A. We show that GRB 230307A belongs to the class of long-duration GRBs associated with compact object mergers4-6 and contains a kilonova similar to AT2017gfo, associated with the GW merger GW170817 (refs. 7-12). We obtained James Webb Space Telescope (JWST) mid-infrared imaging and spectroscopy 29 and 61 days after the burst. The spectroscopy shows an emission line at 2.15 microns, which we interpret as tellurium (atomic mass A = 130) and a very red source, emitting most of its light in the mid-infrared owing to the production of lanthanides. These observations demonstrate that nucleosynthesis in GRBs can create r-process elements across a broad atomic mass range and play a central role in heavy-element nucleosynthesis across the Universe.

A structured jet explains the extreme GRB 221009A.

Long-duration gamma-ray bursts (GRBs) are powerful cosmic explosions, signaling the death of massive stars. Among them, GRB 221009A is by far the brightest burst ever observed. Because of its enormous energy (Eiso ≈ 1055 erg) and proximity (z ≈ 0.15), GRB 221009A is an exceptionally rare event that pushes the limits of our theories. We present multiwavelength observations covering the first 3 months of its afterglow evolution. The x-ray brightness decays as a power law with slope ≈t-1.66, which is not consistent with standard predictions for jetted emission. We attribute this behavior to a shallow energy profile of the relativistic jet. A similar trend is observed in other energetic GRBs, suggesting that the most extreme explosions may be powered by structured jets launched by a common central engine.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning.

INTRODUCTION: Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, in order to find and extract relevant articles from the pack. This task is critical, as serious Adverse Drug Reactions (ADRs) still account for a large number of hospital admissions each year. OBJECTIVES: The aim of this study is to develop an augmented intelligence methodology for automatically identifying relevant publications mentioning an established link between a Drug and a Serious Adverse Event, according to the European Medicines Agency (EMA) definition of seriousness. METHODS: The proposed pipeline, called LiSA (for Literature Search Application), is based on three independent deep learning models supporting a precise detection of safety signals in the biomedical literature. By combining a Bidirectional Encoder Representations from Transformers (BERT) algorithms and a modular architecture, the pipeline achieves a precision of 0.81 and a recall of 0.89 at sentences level in articles extracted from PubMed (either abstract or full-text). We also measured that by using LiSA, a medical reviewer increases by a factor of 2.5 the number of relevant documents it can collect and evaluate compared to a simple keyword search. In the interest of re-usability, emphasis was placed on building a modular pipeline allowing the insertion of other NLP modules to enrich the results provided by the system, and extend it to other use cases. In addition, a lightweight visualization tool was developed to analyze and monitor safety signal results. CONCLUSIONS: Overall, the generic pipeline and the visualization tool proposed in this article allows for efficient and accurate monitoring of serious adverse drug reactions from the literature and can easily be adapted to similar pharmacovigilance use cases. To facilitate reproducibility and benefit other research studies, we also shared a first benchmark dataset for Serious Adverse Drug Events detection.

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences.

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.

Multi-analyser detector (MAD) for high-resolution and high-energy powder X-ray diffraction.

For high-resolution powder diffraction in material science, high photon energies are necessary, especially for in situ and in operando experiments. For this purpose, a multi-analyser detector (MAD) was developed for the high-energy beamline P02.1 at PETRA III of the Deutsches Elektronen-Synchrotron (DESY). In order to be able to adjust the detector for the high photon energies of 60 keV, an individually adjustable analyser-crystal setup was designed. The adjustment is performed via piezo stepper motors for each of the ten channels. The detector shows a low and flat background as well as a high signal-to-noise ratio. A range of standard materials were measured for characterizing the performance. Two exemplary experiments were performed to demonstrate the potential for sophisticated structural analysis with the MAD: (i) the structure of a complex material based on strontium niobate titanate and strontium niobate zirconate was determined and (ii) an in situ stroboscopy experiment with an applied electric field on a highly absorbing piezoceramic was performed. These experiments demonstrate the capabilities of the new MAD, which advances the frontiers of the structural characterization of materials.

Fast-current-heating devices to study in situ phase formation in metallic glasses by using high-energy synchrotron radiation.

Details of fast-resistive-heating setups, controlled heating ranging from ∼101 K s-1 to ∼103 K s-1, to study in situ phase transformations (on heating and on cooling) in metallic glasses by high-energy synchrotron x-ray diffraction are discussed. Both setups were designed and custom built at the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden) and have been implemented at the P02.1 Powder Diffraction and Total Scattering Beamline and the P21.1 Swedish Materials Science Beamline at PETRA III storage ring, DESY, Hamburg. The devices are interchangeable at both beamlines. Joule heating is triggered automatically and is timed with the incident beam and detector. The crystallization process can be controlled via a feedback circuit by monitoring the change in the time-dependent resistivity and temperature of glasses. Different ambient atmospheres, such as vacuum and inert gases (He and Ar), can be used to control oxidation and cooling. The main focus of these devices is on understanding the crystallization mechanism and kinetics in metallic glasses, which are brittle and for which fast heating gives defined glass-crystal composites with enhanced plasticity. As an example, phase-transformation sequence(s) in a prototyped Cu-Zr-based metallic glass is described on heating, and a crystalline phase beneficial to the plasticity is identified.

Tuberculosis of the spine--Prospective neurological and patient reported outcome study.

OBJECTIVE: To prospectively investigate the rate of neurological recovery and patient reported outcome of tuberculosis (TB) spine patients following surgery at a tertiary referral hospital. TB spine remains a major cause of neurological impairment in the developing world fuelled by poor socio-economic conditions and HIV co-infection. Although numerous retrospective studies are available, there is a paucity of prospective data regarding rate of neurological improvement and patient reported outcome. METHODS: Twenty adult patients were prospectively recruited. The average age was 44.5 years. Half had co-existent HIV infection. All patients underwent decompressive surgery utilising a variety of anterior and posterior procedures. All received a minimum of 9 months TB medication. They were followed up at 4, 8, 12, 26 and 52 weeks post operatively. Neurological status was monitored by Nurick, mJOA and ASIA systems. Patient reported outcome was monitored by SF36 questionnaires at all-time points. RESULTS: All patients improved neurologically including 4 ASIA A's. By last follow up, 17 could walk as opposed to 5 pre-op. The Nurick average score improved from 5.5 to 1.9 and the mJOA lower limb score 1.8-5.5. The SF36 improved from 31 to 62 over the year with the biggest gains occurring after 3 months. This was confirmed in all domains except pain and social, which improved earlier. CONCLUSIONS: There is a positive prognosis for neurological outcome in TB spine following a variety of surgical decompressive procedures and medical therapy. The majority of the recovery occurs after the 3 months post-operative mark.

A possible relativistic jetted outburst from a massive black hole fed by a tidally disrupted star.

Gas accretion onto some massive black holes (MBHs) at the centers of galaxies actively powers luminous emission, but most MBHs are considered dormant. Occasionally, a star passing too near an MBH is torn apart by gravitational forces, leading to a bright tidal disruption flare (TDF). Although the high-energy transient Sw 1644+57 initially displayed none of the theoretically anticipated (nor previously observed) TDF characteristics, we show that observations suggest a sudden accretion event onto a central MBH of mass about 10(6) to 10(7) solar masses. There is evidence for a mildly relativistic outflow, jet collimation, and a spectrum characterized by synchrotron and inverse Compton processes; this leads to a natural analogy of Sw 1644+57 to a temporary smaller-scale blazar.

CD38 low IgG-secreting cells are precursors of various CD38 high-expressing plasma cell populations.

Despite the important role immunoglobulin G (IgG)-secreting plasma cells play in memory immune responses, the differentiation and homeostasis of these cells are not completely understood. Here, we studied the differentiation of human IgG-secreting cells ex vivo and in vitro, identifying these cells by the cellular affinity matrix technology. Several subpopulations of IgG-secreting cells were identified among the cells isolated from tonsils and bone marrow, particularly differing in the expression levels of CD9, CD19, and CD38. CD38 low IgG-secreting cells were present exclusively in the tonsils. A major fraction of these cells appeared to be early plasma cell precursors, as upon activation of B cells in vitro, IgG secretion preceded up-regulation of CD38, and on tonsillar sections, IgG-containing, CD38 low cells with a plasmacytoid phenotype were found in follicles, where plasma cell differentiation starts. A unitary phenotype of migratory peripheral blood IgG-secreting cells suggests that all bone marrow plasma cell populations share a common precursor cell. These data are compatible with a multistep model for plasma cell differentiation and imply that a common CD38 low IgG-secreting precursor gives rise to a diverse plasma cell compartment.