Seeing the advantage: Visually grounding word embeddings to better capture human semantic knowledge

Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of these models are purely text based, even though the human sensory experience is much richer. In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning. Our analysis shows that visually grounded embedding similarities are more predictive of the human reaction times in a large priming experiment than the purely text-based embeddings. The visually grounded embeddings also correlate well with human word similarity ratings.Importantly, in both experiments we show that he grounded embeddings account for a unique portion of explained variance, even when we include text-based embeddings trained on huge corpora. This shows that visual grounding allows our model to capture information that cannot be extracted using text as the only source of information

Language learning using speech to image retrieval

Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speech but so far most approaches require text. We improve on existing neural network approaches to create visually grounded embeddings for spoken utterances. Using a combination of a multi-layer GRU, importance sampling, cyclic learning rates, ensembling and vectorial self-attention our results show a remarkable increase in image-caption retrieval performance over previous work. Furthermore, we investigate which layers in the model learn to recognise words in the input. We find that deeper network layers are better at encoding word presence, although the final layer has slightly lower performance. This shows that our visually grounded sentence encoder learns to recognise words from the input even though it is not explicitly trained for word recognition

His-tags as Zn(II) binding motifs in a protein-based fluorescent sensor

Fluorescent indicators that allow real-time imaging of Zn(II) in living cells are invaluable tools for understanding Zn(II) homeostasis. Genetically encoded sensors based on fluorescence resonance energy transfer between fluorescent protein domains have important advantages over synthetic probes. We discovered that hexahistidine tags have a strong tendency to dimerize upon binding of Zn(II) in solution and we used this principle to develop a new protein-based sensor for Zn(II). Enhanced cyan and yellow fluorescent proteins were connected by long flexible peptide linkers and His-tags were incorporated at both termini of this fusion protein. The resulting sensor CLY9-2His allows the ratiometric fluorescent detection of Zn(II) in the nanomolar range. In addition, CLY9-2His is selective over the physiologically relevant metal ions Fe(II), Mn(II), Ca(II) and Mg(II). Our approach demonstrates the potential of using small peptides as metal-binding ligands in chelating fluorescent protein chimeras

Bioluminescent detection of viral surface proteins using branched multivalent protein switches

Fast and reliable virus diagnostics is key to prevent the spread of viruses in populations. A hallmark of viruses is the presence of multivalent surface proteins, a property that can be harnessed to control conformational switching in sensor proteins. Here, we introduce a new sensor platform (dark-LUX) for the detection of viral surface proteins consisting of a general bioluminescent framework that can be post-translationally functionalized with separately expressed binding domains. The platform relies on (1) plug-and-play bioconjugation of different binding proteins via SpyTag/SpyCatcher technology to create branched protein structures, (2) an optimized turn-on bioluminescent switch based on complementation of the split-luciferase NanoBiT upon target binding and (3) straightforward exploration of the protein linker space. The influenza A virus (IAV) surface proteins hemagglutinin (HA) and neuraminidase (NA) were used as relevant multivalent targets to establish proof of principle and optimize relevant parameters such as linker properties, choice of target binding domains and the optimal combination of the competing NanoBiT components SmBiT and DarkBiT. The sensor framework allows rapid conjugation and exchange of various binding domains including scFvs, nanobodies and de novo designed binders for a variety of targets, including the construction of a heterobivalent switch that targets the head and stem region of hemagglutinin. The modularity of the platform thus allows straightforward optimization of binding domains and scaffold properties for existing viral targets, and is well suited to quickly adapt bioluminescent sensor proteins to effectively detect newly evolving viral epitopes

Relation between Reactive Surface Sites and Precursor Choice for Area-Selective Atomic Layer Deposition Using Small Molecule Inhibitors

Implementation of vapor/phase dosing of small molecule inhibitors (SMIs) in advanced atomic layer deposition (ALD) cycles is currently being considered for bottom-up fabrication by area-selective ALD. When SMIs are used, it can be challenging to completely block precursor adsorption due to the inhibitor size and the relatively short vapor/phase exposures. Two strategies for precursor blocking are explored: (i) physically covering precursor adsorption sites, i.e., steric shielding, and (ii) eliminating precursor adsorption sites from the surface, i.e., chemical passivation. In this work, it is determined whether steric shielding is enough for effective precursor blocking during area-selective ALD or whether chemical passivation is required as well. At the same time, we address why some ALD precursors are more difficult to block than others. To this end, the blocking of the Al precursor molecules trimethylaluminum (TMA), dimethylaluminum isopropoxide (DMAI), and tris(dimethylamino)aluminum (TDMAA) was studied by using acetylacetone (Hacac) as inhibitor. It was found that DMAI and TDMAA are more easily blocked than TMA because they adsorb on the same surface sites as Hacac, while TMA is also reactive with other surface sites. This work shows that chemical passivation plays a crucial role for precursor blocking in concert with steric shielding. Moreover, the reactivity of the precursor with the surface groups on the non-growth area dictates the effectiveness of blocking precursor adsorption

High-throughput analysis of subtelomeric chromosome rearrangements by use of array-based comparative genomic hypridization

Telomeric chromosome rearrangements may cause mental retardation, congenital anomalies, and miscarriages. Automated detection of subtle deletions or duplications involving telomeres is essential for high-throughput diagnosis, but impossible when conventional cytogenetic methods are used. Array-based comparative genomic hybridization (CGH) allows high-resolution screening of copy number abnormalities by hybridizing differentially labeled test and reference genomes to arrays of robotically spotted clones. To assess the applicability of this technique in the diagnosis of (sub)telomeric imbalances, we here describe a blinded study, in which DNA from 20 patients with known cytogenetic abnormalities involving one or more telomeres was hybridized to an array containing a validated set of human-chromosome–specific (sub)telomere probes. Single-copy-number gains and losses were accurately detected on these arrays, and an excellent concordance between the original cytogenetic diagnosis and the array-based CGH diagnosis was obtained by use of a single hybridization. In addition to the previously identified cytogenetic changes, array-based CGH revealed additional telomere rearrangements in 3 of the 20 patients studied. The robustness and simplicity of this array-based telomere copy-number screening make it highly suited for introduction into the clinic as a rapid and sensitive automated diagnostic procedure

Immediate implant placement in edentulous oral cancer patients: a long-term retrospective analysis of 207 patients

Although the functional benefits of implants in the rehabilitation of edentulous cancer patients are well-known, most studies report on postponed implant placement. The outcome of immediate implant placement regarding successful rehabilitation, implant loading and survival is unclear. Two hundred and seven edentulous oral cancer patients that received implants during ablative surgery at the Radboud University Medical Centre between 2000 and 2011 were included. Data regarding the oncological treatment, implant placement, follow-up and prosthodontic rehabilitation were recorded retrospectively with a follow-up period of 5–17 years. Functioning implant-retained dentures were made in 73.9% of the patients. Of the surviving patients, 81.9% had functioning dentures after 2 years and 86.3% after 10 years. Patients with ASA score 1 and younger patients were rehabilitated more frequently. The median time of functioning denture placement was 336 days after surgery, with a negative influence of postoperative radiotherapy. Implant survival was 90.7%, and was lower when the implant was placed in a jaw involved in the tumour. Immediate implant placement during oral cancer surgery led to a high number of edentulous patients rehabilitated with implant-retained dentures, which are placed at an early time

Macrocyclization of enzyme-based supramolecular polymers

AB type monomers for supramolecular polymers have been developed based on the strong and reversible noncovalent interaction between ribonuclease S-peptide (A) and S-protein (B), resulting in an active enzyme complex as the linking unit. Two AB-type protein constructs are synthesized differing in the length of the flexible oligo(ethylene glycol) spacer separating the two end groups. Using an experimental setup where size exclusion chromatography is directly coupled to Q-TOF mass spectrometry, we have analyzed the self-assembled architectures as a function of concentration. The theory of macrocyclization under thermodynamic control is used to quantitatively analyze the experimental data. Using this theory, we show that AB-type monomers linked by flexible linkers grow reversibly via ring-chain competition. Inherently the formation of linear polymeric assemblies is beyond the capability of these types of building blocks due to concentration limits of proteins. The results therefore contribute to the general understanding of supramolecular polymerization with biological building blocks and demonstrate design requirements for monomers if linear polymerization is desired