A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease.

The enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitoring of the response to treatment. A commonly used method in clinical practice to estimate the spleen volume is the employment of a formula that uses the measurements of the craniocaudal length, diameter, and thickness of the spleen in MRI. However, the inaccuracy of this formula is significant, which, in turn, emphasizes the need for a more precise and reliable alternative. To this end, we employed deep-learning techniques, to achieve a more accurate spleen segmentation and, subsequently, calculate the resulting spleen volume with higher accuracy on a testing set cohort of 20 patients with GD. Our results indicate that the mean error obtained using the deep-learning approach to spleen volume estimation is 3.6 ± 2.7%, which is significantly lower than the common formula approach, which resulted in a mean error of 13.9 ± 9.6%. These findings suggest that the integration of deep-learning methods into the clinical routine practice for spleen volume calculation could lead to improved diagnostic and monitoring outcomes.

Malaria parasites release vesicle subpopulations with signatures of different destinations.

Malaria is the most serious mosquito-borne parasitic disease, caused mainly by the intracellular parasite Plasmodium falciparum. The parasite invades human red blood cells and releases extracellular vesicles (EVs) to alter its host responses. It becomes clear that EVs are generally composed of sub-populations. Seeking to identify EV subpopulations, we subject malaria-derived EVs to size-separation analysis, using asymmetric flow field-flow fractionation. Multi-technique analysis reveals surprising characteristics: we identify two distinct EV subpopulations differing in size and protein content. Small EVs are enriched in complement-system proteins and large EVs in proteasome subunits. We then measure the membrane fusion abilities of each subpopulation with three types of host cellular membranes: plasma, late and early endosome. Remarkably, small EVs fuse to early endosome liposomes at significantly greater levels than large EVs. Atomic force microscope imaging combined with machine-learning methods further emphasizes the difference in biophysical properties between the two subpopulations. These results shed light on the sophisticated mechanism by which malaria parasites utilize EV subpopulations as a communication tool to target different cellular destinations or host systems.

Hidden signatures of early fire at Evron Quarry (1.0 to 0.8 Mya).

Pyrotechnology is a key element of hominin evolution. The identification of fire in early hominin sites relies primarily on an initial visual assessment of artifacts' physical alterations, resulting in potential underestimation of the prevalence of fire in the archaeological record. Here, we used a suite of spectroscopic techniques to counter the absence of visual signatures for fire and demonstrate the presence of burnt fauna and lithics at the Lower Paleolithic (LP) open-air site of Evron Quarry (Israel), dated between 1.0 and 0.8 Mya and roughly contemporaneous to Gesher Benot Ya'aqov where early pyrotechnology has been documented. We propose reexamining finds from other LP sites lacking visual clues of pyrotechnology to yield a renewed perspective on the origin, evolution, and spatiotemporal dispersal of the relationship between early hominin behavior and fire use.

The role of convolutional neural networks in scanning probe microscopy: a review.

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

20S proteasomes secreted by the malaria parasite promote its growth.

Mature red blood cells (RBCs) lack internal organelles and canonical defense mechanisms, making them both a fascinating host cell, in general, and an intriguing choice for the deadly malaria parasite Plasmodium falciparum (Pf), in particular. Pf, while growing inside its natural host, the human RBC, secretes multipurpose extracellular vesicles (EVs), yet their influence on this essential host cell remains unknown. Here we demonstrate that Pf parasites, cultured in fresh human donor blood, secrete within such EVs assembled and functional 20S proteasome complexes (EV-20S). The EV-20S proteasomes modulate the mechanical properties of naïve human RBCs by remodeling their cytoskeletal network. Furthermore, we identify four degradation targets of the secreted 20S proteasome, the phosphorylated cytoskeletal proteins ß-adducin, ankyrin-1, dematin and Epb4.1. Overall, our findings reveal a previously unknown 20S proteasome secretion mechanism employed by the human malaria parasite, which primes RBCs for parasite invasion by altering membrane stiffness, to facilitate malaria parasite growth.

Estimating temperatures of heated Lower Palaeolithic flint artefacts.

Production of stone artefacts using pyro-technology is known from the Middle and Upper Palaeolithic of Europe and the Levant, and the Middle Stone Age in Africa. However, determination of temperatures to which flint artefacts were exposed is impeded by the chemical and structural variability of flint. Here we combine Raman spectroscopy and machine learning to build temperature-estimation models to infer the degree of pyro-technological control effected by inhabitants of the late Lower Palaeolithic (Acheulo-Yabrudian) site of Qesem Cave, Israel. Temperature estimation shows that blades were heated at lower median temperatures (259 °C) compared to flakes (413 °C), whereas heat-induced structural flint damage (for example, pot-lids and microcracks) appears at 447 °C. These results are consistent with a differential behaviour for selective tool production that can be viewed as part of a plethora of innovative and adaptive behaviours of Levantine hominins >300,000 years ago.

A Division of Labor between YAP and TAZ in Non-Small Cell Lung Cancer.

Lung cancer is the leading cause of cancer-related deaths worldwide. The paralogous transcriptional cofactors Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ, also called WWTR1), the main downstream effectors of the Hippo signal transduction pathway, are emerging as pivotal determinants of malignancy in lung cancer. Traditionally, studies have tended to consider YAP and TAZ as functionally redundant transcriptional cofactors with similar biological impact. However, there is growing evidence that each of them also possesses distinct attributes. Here we sought to systematically characterize the division of labor between YAP and TAZ in non-small cell lung cancer (NSCLC), the most common histological subtype of lung cancer. Representative NSCLC cell lines as well as patient-derived data showed that the two paralogs orchestrated nonoverlapping transcriptional programs in this cancer type. YAP preferentially regulated gene sets associated with cell division and cell-cycle progression, whereas TAZ preferentially regulated genes associated with extracellular matrix organization. Depletion of YAP resulted in growth arrest, whereas its overexpression promoted cell proliferation. Likewise, depletion of TAZ compromised cell migration, whereas its overexpression enhanced migration. The differential effects of YAP and TAZ on key cellular processes were also associated with differential response to anticancer therapies. Uncovering the different activities and downstream effects of YAP and TAZ may thus facilitate better stratification of patients with lung cancer for anticancer therapies. SIGNIFICANCE: Thease findings show that oncogenic paralogs YAP and TAZ have distinct roles in NSCLC and are associated with differential response to anticancer drugs, knowledge that may assist lung cancer therapy decisions.

Mechanical and Tribological Properties of Layered Materials under High Pressure: Assessing the Importance of Many-Body Dispersion Effects.

The importance of many-body dispersion effects in layered materials subjected to high external loads is evaluated. State-of-the-art many-body dispersion density functional theory calculations performed for graphite, hexagonal boron nitride, and their heterostructures were used to fit the parameters of a classical registry-dependent interlayer potential. Using the latter, we performed extensive equilibrium molecular dynamics simulations and studied the mechanical response of homogeneous and heterogeneous bulk models under hydrostatic pressures up to 30 GPa. Comparison with experimental data demonstrates that the reliability of the many-body dispersion model extends deep into the subequilibrium regime. Friction simulations demonstrate the importance of many-body dispersion effects for the accurate description of the tribological properties of layered material interfaces under high pressure.

Mechanically rigid supramolecular assemblies formed from an Fmoc-guanine conjugated peptide nucleic acid.

The variety and complexity of DNA-based structures make them attractive candidates for nanotechnology, yet insufficient stability and mechanical rigidity, compared to polyamide-based molecules, limit their application. Here, we combine the advantages of polyamide materials and the structural patterns inspired by nucleic-acids to generate a mechanically rigid fluorenylmethyloxycarbonyl (Fmoc)-guanine peptide nucleic acid (PNA) conjugate with diverse morphology and photoluminescent properties. The assembly possesses a unique atomic structure, with each guanine head of one molecule hydrogen bonded to the Fmoc carbonyl tail of another molecule, generating a non-planar cyclic quartet arrangement. This structure exhibits an average stiffness of 69.6 ± 6.8 N m-1 and Young's modulus of 17.8 ± 2.5 GPa, higher than any previously reported nucleic acid derived structure. This data suggests that the unique cation-free "basket" formed by the Fmoc-G-PNA conjugate can serve as an attractive component for the design of new materials based on PNA self-assembly for nanotechnology applications.

Terahertz spectroscopy of 2,4,6-trinitrotoluene molecular solids from first principles.

We present a computational analysis of the terahertz spectra of the monoclinic and the orthorhombic polymorphs of 2,4,6-trinitrotoluene. Very good agreement with experimental data is found when using density functional theory that includes Tkatchenko-Scheffler pair-wise dispersion interactions. Furthermore, we show that for these polymorphs the theoretical results are only weakly affected by many-body dispersion contributions. The absence of dispersion interactions, however, causes sizable shifts in vibrational frequencies and directly affects the spatial character of the vibrational modes. Mode assignment allows for a distinction between the contributions of the monoclinic and orthorhombic polymorphs and shows that modes in the range from 0 to ca. 3.3 THz comprise both inter- and intramolecular vibrations, with the former dominating below ca. 1.5 THz. We also find that intramolecular contributions primarily involve the nitro and methyl groups. Finally, we present a prediction for the terahertz spectrum of 1,3,5-trinitrobenzene, showing that a modest chemical change leads to a markedly different terahertz spectrum.

Bioinspired Flexible and Tough Layered Peptide Crystals.

One major challenge of functional material fabrication is combining flexibility, strength, and toughness. In several biological and artificial systems, these desired mechanical properties are achieved by hierarchical architectures and various forms of anisotropy, as found in bones and nacre. Here, it is reported that crystals of N-capped diphenylalanine, one of the most studied self-assembling systems in nanotechnology, exhibit well-ordered packing and diffraction of sub-Å resolution, yet display an exceptionally flexible nature. To explore this flexibility, the mechanical properties of individual crystals are evaluated, assisted by density functional theory calculations. High-resolution scanning electron microscopy reveals that the crystals are composed of layered self-assembled structures. The observed combination of strength, toughness, and flexibility can therefore be explained in terms of weak interactions between rigid layers. These crystals represent a novel class of self-assembled layered materials, which can be utilized for various technological applications, where a combination of usually contradictory mechanical properties is desired.

Real-space pseudopotential method for computing the vibrational Stark effect.

The vibrational Stark shift is an important effect in determining the electrostatic environment for molecular or condensed matter systems. However, accurate ab initio calculations of the vibrational Stark effect are a technically demanding challenge. We make use of density functional theory constructed on a real-space grid to expedite the computation of this effect. Our format is especially advantageous for the investigation of small molecules in finite fields as cluster boundary conditions eliminate spurious supercell interactions and allow for charged systems, while convergence is controlled by a single parameter, the grid spacing. The Stark tuning rate is highly sensitive to the interaction between anharmonicity in a vibrational mode and the applied field. To ensure this subtle interaction is fully captured, we apply three parallel approaches: a direct finite field, a perturbative method, and a molecular dynamics method. We illustrate this method by applying it to several small molecules containing C-O and C-N bonds and show that a consistent result can be obtained.

Interlayer Potential for Graphene/h-BN Heterostructures.

We present a new force-field potential that describes the interlayer interactions in heterojunctions based on graphene and hexagonal boron nitride (h-BN). The potential consists of a long-range attractive term and a short-range anisotropic repulsive term. Its parameters are calibrated against reference binding and sliding energy profiles for a set of finite dimer systems and the periodic graphene/h-BN bilayer, obtained from density functional theory using a screened-exchange hybrid functional augmented by a many-body dispersion treatment of long-range correlation. Transferability of the parametrization is demonstrated by considering the binding energy of bulk graphene/h-BN alternating stacks. Benchmark calculations for the superlattice formed when relaxing the supported periodic heterogeneous bilayer provide good agreement with both experimental results and previous computational studies. For a free-standing bilayer we predict a highly corrugated relaxed structure. This, in turn, is expected to strongly alter the physical properties of the underlying monolayers. Our results demonstrate the potential of the developed force-field to model the structural, mechanical, tribological, and dynamic properties of layered heterostructures based on graphene and h-BN.

Unusually Large Young's Moduli of Amino Acid Molecular Crystals.

Young's moduli of selected amino acid molecular crystals were studied both experimentally and computationally using nanoindentation and dispersion-corrected density functional theory. The Young modulus is found to be strongly facet-dependent, with some facets exhibiting exceptionally high values (as large as 44 GPa). The magnitude of Young's modulus is strongly correlated with the relative orientation between the underlying hydrogen-bonding network and the measured facet. Furthermore, we show computationally that the Young modulus can be as large as 70-90 GPa if facets perpendicular to the primary direction of the hydrogen-bonding network can be stabilized. This value is remarkably high for a molecular solid and suggests the design of hydrogen-bond networks as a route for rational design of ultra-stiff molecular solids.

Inter-layer potential for hexagonal boron nitride.

A new interlayer force-field for layered hexagonal boron nitride (h-BN) based structures is presented. The force-field contains three terms representing the interlayer attraction due to dispersive interactions, repulsion due to anisotropic overlaps of electron clouds, and monopolar electrostatic interactions. With appropriate parameterization, the potential is able to simultaneously capture well the binding and lateral sliding energies of planar h-BN based dimer systems as well as the interlayer telescoping and rotation of double walled boron-nitride nanotubes of different crystallographic orientations. The new potential thus allows for the accurate and efficient modeling and simulation of large-scale h-BN based layered structures.

Why are diphenylalanine-based peptide nanostructures so rigid? Insights from first principles calculations.

The diphenylalanine peptide self-assembles to form nanotubular structures of remarkable mechanical, piezolelectrical, electrical, and optical properties. The tubes are unexpectedly stiff, with reported Young's moduli of 19-27 GPa that were extracted using two independent techniques. Yet the physical basis for the remarkable rigidity is not fully understood. Here, we calculate the Young's modulus for bulk diphenylalanine peptide from first principles, using density functional theory with dispersive corrections. The calculation demonstrates that at least half of the stiffness of the material is the result of dispersive interactions. We further quantify the nature of various inter- and intramolecular interactions. We reveal that despite the porous nature of the lattice, there is an array of rigid nanotube backbones with interpenetrating "zipper-like" aromatic interlocks that result in stiffness and robustness. This presents a general strategy for the analysis of bioinspired functional materials and may pave the way for rational design of bionanomaterials.