A neutralizing single-domain antibody that targets the trimer interface of the human metapneumovirus fusion protein.

IMPORTANCE: Human metapneumovirus (hMPV) is an important respiratory pathogen for which no licensed antivirals or vaccines exist. Single-domain antibodies represent promising antiviral biologics that can be easily produced and formatted. We describe the isolation and detailed characterization of two hMPV-neutralizing single-domain antibodies that are directed against the fusion protein F. One of these single-domain antibodies broadly neutralizes hMPV A and B strains, can prevent proteolytic maturation of F, and binds to an epitope in the F trimer interface. This suggests that hMPV pre-F undergoes trimer opening or "breathing" on infectious virions, exposing a vulnerable site for neutralizing antibodies. Finally, we show that this single-domain antibody, fused to a human IgG1 Fc, can protect cotton rats against hMPV replication, an important finding for potential future clinical applications.

Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks.

The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach to finding stable inorganic functional materials. We start by defining an upper bound to the fully relaxed energy obtained via DFT as the energy resulting from a constrained optimization over only cell volume. Because the fractional atomic coordinates for these calculations are known a priori, this upper bound energy can be quickly and accurately predicted with a scale-invariant graph neural network (GNN). We generate new structures via ionic substitution of known prototypes, and train our GNN on a new database of 128 000 DFT calculations comprising both fully relaxed and volume-only relaxed structures. By minimizing the predicted upper-bound energy, we discover new stable structures with over 99% accuracy (versus DFT). We demonstrate the method by finding promising new candidates for solid-state battery (SSB) electrolytes that not only possess the required stability, but also additional functional properties such as large electrochemical stability windows and high conduction ion fraction. We expect this proposed framework to be directly applicable to a wide range of design challenges in materials science.

Implicit angry faces interfere with response inhibition and response adjustment.

Cognitive control enables people to adjust their thoughts and actions according to the current task demands. Response inhibition and response adjustment are two key aspects of cognitive control. Here, we examined how the implicit processing of emotional information influences these two functions with the help of the double-step saccade task. Each trial had either a single target or two sequential targets. Upon a single target onset, participants were required to make a quick saccade, but upon two target onsets, participants were instructed to inhibit their initial saccades and redirect their gaze to the second target. In three experiments, we manipulated the emotional information of the first and second targets. We found that irrelevant emotional information of the first target impaired response inhibition compared to non-emotional information (geometric shapes) of the first target. When non-emotional information (geometric shape) came as the first target, irrelevant angry emotional faces as the second target interfered with both response inhibition and response adjustment compared to irrelevant happy and neutral faces. We explain these results with previous findings that processing faces with irrelevant angry facial expressions take up many attentional resources, leaving fewer resources available for ongoing activities such as response inhibition and response adjustment.

Irrelevant angry faces impair response inhibition, and the go and stop processes share attentional resources.

Response inhibition is a crucial component of executive control, which refers to our ability to suppress responses that are no longer needed or inappropriate. The stop-signal task is a standard tool to assess inhibitory control over actions. Here, we use irrelevant facial expressions (happy, angry, or neutral) as both go and stop-signal to examine competition for shared attentional resources between (a) emotion and inhibition process and (b) go and stop processes. Participants were required to respond to go signals (gender discrimination task: male or female). Occasionally, a stop-signal (face with irrelevant angry, happy, or neutral facial expression) was presented, and participants were required to withhold their motor response. We found that emotion processing (especially angry faces) captures attention away from the task, and the emotionality of the stop signal matters only when the go signal is non-emotional. When the go signal was non-emotional, we found that stop-signal with irrelevant angry facial expressions impaired inhibitory control compared to stop-signal with irrelevant happy and neutral facial expressions. These results indicate that the processing of emotion and inhibition process exploit a shared pool of attentional resources. These results favor an interactive capacity-sharing account of the go and stop processes in models of response inhibition.

Design, synthesis and biological evaluation of new quinazolinone-benzopyran-indole hybrid compounds promoting osteogenesis through BMP2 upregulation.

In search of novel osteogenic entities, a series of twenty-seven quinazolinone-benzopyran-indole hybrids were designed and synthesized using molecular hybridization approach. All the compounds were scrutinized for their osteogenic potential, primarily based on alkaline phosphatase assay as one of the major anabolic markers. From the primary screening, four osteogenic compounds were sorted from the series and were found nontoxic to the osteoblasts. Further, increased osteoblast differentiation and osteogenic mRNA upregulations suggest compound 47 as the most potent osteoanabolic agent. Immunoblot and ELISA analysis demonstrated that compound 47 promotes osteogenesis via RUNX2 and BMP2 mediated non-canonical p38 pathway. In vivo studies in BALB/c mice inferred that compound 47 stimulates bone anabolism as evident from histological and gene expression studies at 5 mg. kg-1. day-1 dose. Furthermore, structural activity relationship (SAR) and pharmacokinetic studies suggest compound 47 as a BMP2 upregulator and a potential bone anabolic lead for combating future bone metabolic disorders.

Irrelevant positive emotional information facilitates response inhibition only under a high perceptual load.

Response inhibition involves suppressing those responses that are no longer needed. Previous research has separately studied the role of attentional resources and emotional information in response inhibition. Here, we simultaneously manipulate attentional resources and emotional information to investigate the interactive role of emotional information and attentional resources. Attentional resources were manipulated by changing the levels of perceptual load (low and high) of go signals. Emotional information was manipulated by changing the emotional content (irrelevant positive and negative emotional information) of the stop signals. Participants made a go response based on searching for a target letter in conditions of either low perceptual load or high perceptual load. They withheld their response on the presentation of a stop signal. The stop-signal stimulus was selected from two classes: arousal matched positive and negative IAPS images (Experiment 1) and happy, angry, and neutral faces (Experiment 2). The result showed a consistent interaction pattern of perceptual load and emotional information across the two experiments, such that irrelevant positive emotional information consistently improved inhibitory control, albeit only under high load. These results have theoretical implications for understanding the nature of emotional information and their interaction with attentional resources in cognitive control functions.

Recent advances in the synthesis of thienoindole analogs and their diverse applications.

Thiophene-fused heterocyclic organosulfur systems, especially the thieno[3,2-b]indole moiety have attracted significant attention because they show a wide spectrum of biological activities such as antituberculosis, antitumor, antifungal, antibacterial, and human 5-HT5A receptor binding inhibition. Moreover, they also find applications in material chemistry and chemical engineering. Thus, due to their intriguing properties and applications, researchers are continually attempting to create more effective and environment-friendly methods for their preparation. In this review, we present a complete assessment of the current advances in the field of thieno[3,2-b]indole synthesis.

Feeder Cells at the Interface of Natural Killer Cell Activation, Expansion and Gene Editing.

Genome engineered natural killer (NK) cell therapies are emerging as a promising cancer immunotherapy platform with potential advantages and remaining uncertainties. Feeder cells induce activation and proliferation of NK cells via cell surface receptor-ligand interactions, supported by cytokines. Feeder cell expanded NK cell products have supported several NK cell adoptive transfer clinical trials over the past decade. Genome engineered NK cell therapies, including CAR-NK cells, seek to combine innate and alloreactive NK cell anti-tumor activity with antigen specific targeting or additional modifications aimed at improving NK cell persistence, homing or effector function. The profound activating and expansion stimulus provided by feeder cells is integral to current applications of clinical-scale genome engineering approaches in donor-derived, primary NK cells. Herein we explore the complex interactions that exist between feeder cells and both viral and emerging non-viral genome editing technologies in NK cell engineering. We focus on two established clinical-grade feeder systems; Epstein-Barr virus transformed lymphoblastoid cell lines and genetically engineered K562.mbIL21.4-1BBL feeder cells.

Predicting energy and stability of known and hypothetical crystals using graph neural network.

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerated screening. Here, we demonstrate the importance of a balanced training dataset of GS and higher-energy structures to accurately predict total energies using a generic graph neural network architecture. Using ∼ 16,500 density functional theory calculations from the National Renewable Energy Laboratory (NREL) Materials Database and ∼ 11,000 calculations for hypothetical structures as our training database, we demonstrate that our model satisfactorily ranks the structures in the correct order of total energies for a given composition. Furthermore, we present a thorough error analysis to explain failure modes of the model, including both prediction outliers and occasional inconsistencies in the training data. By examining intermediate layers of the model, we analyze how the model represents learned structures and properties.

Heterometallic Actinide-Containing Photoresponsive Metal-Organic Frameworks: Dynamic and Static Tuning of Electronic Properties.

Acquiring fundamental knowledge of properties of actinide-based materials is a necessary step to create new possibilities for addressing the current challenges in the nuclear energy and nuclear waste sectors. In this report, we established a photophysics-electronics correlation for actinide-containing metal-organic frameworks (An-MOFs) as a function of excitation wavelength, for the first time. A stepwise approach for dynamically modulating electronic properties was applied for the first time towards actinide-based heterometallic MOFs through integration of photochromic linkers. Optical cycling, modeling of density of states near the Fermi edge, conductivity measurements, and photoisomerization kinetics were employed to shed light on the process of tailoring optoelectronic properties of An-MOFs. Furthermore, the first photochromic MOF-based field-effect transistor, in which the field-effect response could be changed through light exposure, was constructed. As a demonstration, the change in current upon light exposure was sufficient to operate a two-LED fail-safe indicator circuit.

A Novel Three-Dimensional Interactive Virtual Face to Facilitate Facial Anatomy Teaching Using Microsoft HoloLens.

BACKGROUND: In-depth understanding of facial anatomy is the foundation of clinical education and practices. Lately, anatomy education has undergone many changes due to the adoption of integrated medical curriculums. The time allocated to anatomy teaching on occasion been shortened to allow more time for clinical education. Innovation in visual technology such as virtual reality (VR), augmented reality (AR) and mixed reality (MR) has added a new dimension to anatomy education. The authors present a mixed reality virtual face model to facilitate complex anatomy teaching using Microsoft HoloLens in various educational settings. MATERIALS AND METHODS: Close-range photogrammetry technique was utilised to construct a virtual face using a dissected fresh-frozen cephalus. Scanning was undertaken using DSLR cameras, capable of capturing ten frames per second, in ten different angles on the dissected side and four for the undissected side. The images of the entire cephalous were also separately captured in twenty different angles. These images were processed using software to reconstruct the three-dimensional virtual face. RESULT: The virtual face using a mixed reality platform was able to demonstrate individual layers of the face and relevant clinically significant structures clearly with interactive labelling. Face and the content validity by 12 experts (plastic surgeons and dermatologists) demonstrated strong inter-rater reliability expressed as interclass correlation coefficient. CONCLUSION: An immersive experience by using Microsoft HoloLens provides an accurate 3D perception of the face to enhance anatomy learning. This can be utilised in undergraduate, postgraduate and continued medical education as an additional teaching tool in the constraints of cadaveric dissection. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Calix[4]amido crown functionalized visible sensors for cyanide and iodide anions.

This study comprises the design and development of calix[4] arene-amido-based ionophores by varying structural stringency and steric hindrance at the lower rim to probe the anion sensing properties. The ionophores are prepared, purified, and characterized using various analytical techniques. The molecular structure of the most active ionophore I is established by single-crystal X-ray characterisation. Out of various anions investigated, iodide and cyanide show the highest sensitivity towards the ionophores investigated. Both anions are sensitive enough to give a visibly distinct color change. The binding properties of the ionophores are established with 1H & 127I NMR, fluorescence, and UV-vis spectroscopy, revealing that three ionophores strongly interact with CN- and I-. The binding constants are calculated via Benesi-Hildebrand plots using absorption data. The time-dependent 1H NMR revealed strong hydrogen bonding between the OH and NH groups of the ionophore and cyanide anion. The 127I NMR shows the highest 27.6 ppm shift after 6 h for ionophore I. The crystal structure revealed hydrogen bonding of N-H protons of the amide pendulum and phenolic oxygen of the calix rim. The Job's plot depicted the possibility of a 1 : 1 complex of ionophores with both anions.

Microwave-assisted efficient synthesis of pyrazole-fibrate derivatives as stimulators of glucose uptake in skeletal muscle cells.

The design and synthesis of a series of pyrazolo[3,4-d]pyrimidinones containing fibrate side chains have been accomplished by utilizing the concept of molecular hybridization. All the synthesized compounds were evaluated for the glucose uptake stimulatory effect in L6 rat skeletal muscle cells. Four compounds (3f, 3g, 3j and 3q) were found to show significant stimulation of glucose uptake. Further these four compounds have been examined for their Glut4 translocation stimulatory effect in L6-Glut4myc myotubes. Compound 3q was found to exert maximum increase in GLUT4myc translocation.

Pupil tracks statistical regularities: behavioral and neural implications.

Pupillary light reflex adjusts the amount of light reaching the retina. Recent work suggests that the brainstem pupillary light reflex pathway is controlled by the environment's internal models derived from higher-order temporal statistics. This finding has implications at the behavioral and neural levels. Pupillary changes in response to statistical regularities could be a metric constituting the precision with which the internal models are represented. These pupillary changes may aid in information processing through attentional mechanisms. One possible region that mediates descending cognitive inputs to pupil cycling is locus coeruleus. Here we propose a unified framework of locus coeruleus' role in modulating pupillary change, which successfully explains current and previous findings. The locus coeruleus could have multiple subsystems selectively (but not exclusively) driven by behavioral relevance and statistical learning to regulate pupillary change.

Artificial intelligence and machine learning for protein toxicity prediction using proteomics data.

Instead of only focusing on the targeted drug delivery system, researchers have a great interest in developing peptide-based therapies for the procurement of numerous class of diseases. The main idea behind this is to anchor the properties of the receptor to design peptide-based therapeutics. As these macromolecules have distinct physicochemical properties over small molecules, it becomes an obligatory field for the treatment of diseases. For this, various in silico models have been developed to speculate the proteins by virtue of the application of machine learning and artificial intelligence. By analysing the properties and structural alert of toxic proteins, researchers aim to dissert some of the mechanisms of protein toxicity from which therapeutic insights may be drawn. Numerous models already exist worldwide emphasizing themselves as leading paramount for toxicity prediction in protein macromolecules. Few of them comparatively compete with the other predictive protein toxicity models and convincingly give a high-performance result in terms of accuracy. But their foundation is quite ambiguous, and varying approaches are found at the level of toxicoproteomic data utilization while building a machine learning model. In this review work, we present the contribution of artificial intelligence and machine learning approaches in prediction of protein toxicity using proteomics data.

Evaluating single-domain antibodies as carriers for targeted vaccine delivery to the small intestinal epithelium.

Targeting a vaccine to the mucosal surface has recently been recognized as a promising approach to efficiently induce mucosal immune responses against enteric pathogens. However, poor uptake and inefficient transport of orally delivered subunit vaccines across the intestinal epithelium combined with weak immune responses still present important bottlenecks for mucosal vaccination. A possible strategy suggested to surmount these hurdles is to target the selected antigen to transcytotic receptors, such as aminopeptidase N (APN) present on enterocytes and antigen-presenting cells (APCs). Therefore, we aimed to identify potent and selective VHHs against porcine aminopeptidase N (pAPN), that were fused to the fragment crystallizable (Fc) domain of the murine IgG2a, resulting in dimeric VHH-MG fusions. Out of a library of 30 VHH-MG fusion candidates, two fusions displaying the best binding on pAPN-expressing cells were selected and showed in vivo internalization across the porcine gut epithelium. One of these fusions triggered systemic and intestinal IgA responses upon oral administration. Our results demonstrate the potential of bivalent VHH-MG fusions as delivery vehicles for vaccine antigens. VHH-mediated targeting of antigens to APN to generate protective immunity at the mucosal surface remains to be further validated.

Heterometallic multinuclear nodes directing MOF electronic behavior.

Metal node engineering in combination with modularity, topological diversity, and porosity of metal-organic frameworks (MOFs) could advance energy and optoelectronic sectors. In this study, we focus on MOFs with multinuclear heterometallic nodes for establishing metal-property trends, i.e., connecting atomic scale changes with macroscopic material properties by utilization of inductively coupled plasma mass spectrometry, conductivity measurements, X-ray photoelectron and diffuse reflectance spectroscopies, and density functional theory calculations. The results of Bader charge analysis and studies employing the Voronoi-Dirichlet partition of crystal structures are also presented. As an example of frameworks with different nodal arrangements, we have chosen MOFs with mononuclear, binuclear, and pentanuclear nodes, primarily consisting of first-row transition metals, that are incorporated in HHTP-, BTC-, and NIP-systems, respectively (HHTP3- = triphenylene-2,3,6,7,10,11-hexaone; BTC3- = 1,3,5-benzenetricarboxylate; and NIP2- = 5-nitroisophthalate). Through probing framework electronic profiles, we demonstrate structure-property relationships, and also highlight the necessity for both comprehensive analysis of trends in metal properties, and novel avenues for preparation of heterometallic multinuclear isoreticular structures, which are critical components for on-demand tailoring of properties in heterometallic systems.

Thermodynamics and Electronic Properties of Heterometallic Multinuclear Actinide-Containing Metal-Organic Frameworks with "Structural Memory".

Thermodynamic studies of actinide-containing metal-organic frameworks (An-MOFs), reported herein for the first time, are a step toward addressing challenges related to effective nuclear waste administration. In addition to An-MOF thermochemistry, enthalpies of formation were determined for the organic linkers, 2,2'-dimethylbiphenyl-4,4'-dicarboxylic acid (H2Me2BPDC) and biphenyl-4,4'-dicarboxylic acid (H2BPDC), which are commonly used building blocks for MOF preparation. The electronic structure of the first example of An-MOF with mixed-metal AnAn'-nodes was influenced through coordination of transition metals as shown by the density of states near the Fermi edge, changes in the Tauc plot, conductivity measurements, and theoretical calculations. The "structural memory" effect (i.e., solvent-directed crystalline-amorphous-crystalline structural dynamism) was demonstrated as a function of node coordination degree, which is the number of organic linkers per metal node. Remarkable three-month water stability was reported for Th-containing frameworks herein, and the mechanism is also considered for improvement of the behavior of a U-based framework in water. Mechanistic aspects of capping linker installation were highlighted through crystallographic characterization of the intermediate, and theoretical calculations of free energies of formation (ΔGf) for U- and Th-MOFs with 10- and 12-coordinated secondary building units (SBUs) were performed to elucidate experimentally observed transformations during the installation processes. Overall, these results are the first thermochemical, electronic, and mechanistic insights for a relatively young class of actinide-containing frameworks.

Galvanic Replacement-Driven Transformations of Atomically Intermixed Bimetallic Colloidal Nanocrystals: Effects of Compositional Stoichiometry and Structural Ordering.

Galvanic replacement reactions dictated by deliberately designed nanoparticulate templates have emerged as a robust and versatile approach that controllably transforms solid monometallic nanocrystals into a diverse set of architecturally more sophisticated multimetallic hollow nanostructures. The galvanic atomic exchange at the nanoparticle/liquid interfaces induces a series of intriguing structure-transforming processes that interplay over multiple time and length scales. Using colloidal Au-Cu alloy and intermetallic nanoparticles as structurally and compositionally fine-tunable bimetallic sacrificial templates, we show that atomically intermixed bimetallic nanocrystals undergo galvanic replacement-driven structural transformations remarkably more complicated than those of their monometallic counterparts. We interpret the versatile structure-transforming behaviors of the bimetallic nanocrystals in the context of a unified mechanistic picture that rigorously interprets the interplay of three key structure-evolutionary pathways: dealloying, Kirkendall diffusion, and Ostwald ripening. By deliberately tuning the compositional stoichiometry and atomic-level structural ordering of the Au-Cu bimetallic nanocrystals, we have been able to fine-maneuver the relative rates of dealloying and Kirkendall diffusion with respect to that of Ostwald ripening through which an entire family of architecturally distinct complex nanostructures are created in a selective and controllable manner upon galvanic replacement reactions. The insights gained from our systematic comparative studies form a central knowledge framework that allows us to fully understand how multiple classic effects and processes interplay within the confinement by a colloidal nanocrystal to synergistically guide the structural transformations of complex nanostructures at both the atomic and nanoparticulate levels.