Browse Articles

Prediction of inhibitory peptides against E.coli with desired MIC value

Characterization of visual cognition in pre-manifest, manifest and reduced penetrance Huntington’s disease

Efficacy and safety of the enhanced monofocal intraocular lens in glaucoma of varying severity

Abstract This study evaluates the safety and visual outcomes of enhanced monofocal intraocular lenses compared to standard monofocal intraocular lenses in patients with varying severities of glaucoma. Utilizing data from surgeries performed in 2021, the study involved patients aged 40 and older with visually significant cataracts and diagnosed glaucoma or glaucoma suspects. The findings indicate that both enhanced and standard monofocal lenses lead to significant improvements in best-corrected visual acuity, visual field index, mean deviation, and retinal nerve fiber layer thickness postoperatively. No significant differences were observed between the two groups in the extent of these improvements, suggesting that enhanced monofocal lenses may offer a viable alternative for patients with glaucoma, providing significant visual benefits and potentially better intermediate vision while preserving overall visual function.

Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation

Looking outside the box with a pathology aware AI approach for analyzing OCT retinal images in Stargardt disease

Abstract Stargardt disease type 1 (STGD1) is a genetic disorder that leads to progressive vision loss, with no approved treatments currently available. The development of effective therapies faces the challenge of identifying appropriate outcome measures that accurately reflect treatment benefits. Optical Coherence Tomography (OCT) provides high-resolution retinal images, serving as a valuable tool for deriving potential outcome measures, such as retinal thickness. However, automated segmentation of OCT images, particularly in regions disrupted by degeneration, remains complex. In this study, we propose a deep learning-based approach that incorporates a pathology-aware loss function to segment retinal sublayers in OCT images from patients with STGD1. This method targets relatively unaffected regions for sublayer segmentation, ensuring accurate boundary delineation in areas with minimal disruption. In severely affected regions, identified by a box detection model, the total retina is segmented as a single layer to avoid errors. Our model significantly outperforms standard models, achieving an average Dice coefficient of $$99\%$$ for total retina and $$93\%$$ for retinal sublayers. The most substantial improvement was in the segmentation of the photoreceptor inner segment, with Dice coefficient increasing by $$25\%$$ . This approach provides a balance between granularity and reliability, making it suitable for clinical application in tracking disease progression and evaluating therapeutic efficacy.

MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection

Carboxymethyl cellulose assisted reforming of poly acrylic acid co methyl methacrylate composite for wastewater treatment and effective hosting of antimicrobial silver

Abstract Herein, novel polymer composite is fabricated by hybridizing poly (acrylic acid-co-methyl methacrylate) filaments with carboxymethyl cellulose, which efficiently reorients and strictly ties the fibrous chains to form polymeric units of plate-like morphology. This innovative hybrid polymer composite is analyzed using XRD, FT-IR, swelling and contact angle studies, DLS, AFM, and SEM. Removal efficiency of such polymer composite is scrutinized in colored wastewater treatment. Langmuir and pseudo-first-order kinetic models best describe safranine dye removal from wastewater, adopting exothermic adsorption progression with elevated capacity (~ 59.47 mg/g) and accelerated rate (~ 1.06 h − 1 ). Such polymer composite exhibits persistent removal efficiency of ~ 90% within 10 min for five consecutive cycles. Hybrid polymer composite is good candidate platform for hosting Ag particles to heighten their antimicrobial activity against Escherichia coli and Staphylococcus aureus , far exceeding 75% reduction. Future studies on applicability of oxygen-rich polymer composites in wastewater treatment and disinfection are optimistic and extremely competent.

Characterization of the genomic landscape of canine diffuse large B-cell lymphoma reveals recurrent H3K27M mutations linked to progression-free survival

Abstract Diffuse large B-cell lymphoma (DLBCL) is an aggressive hematopoietic neoplasm that affects humans as well as dogs. While previous studies on canine DLBCL (cDLBCL) have significantly advanced our understanding of the disease, the majority of this research has relied on whole-exome sequencing, which is limited in its ability to detect copy number aberrations and other genomic changes beyond coding regions. Furthermore, many of these studies lack sufficient clinical follow-up data, making it difficult to draw meaningful associations between genetic variants and patient outcomes. Our study aimed to characterize the mutational landscape of cDLBCL using whole-genome sequencing of matched tumor-normal samples obtained from a cohort of 43 dogs previously enrolled in a clinical trial for which longitudinal follow-up was available. We focused on identifying genes that were significantly or recurrently mutated with coding point mutations, copy number aberrations, and their associations with patient outcomes. We identified 26 recurrently mutated genes, 18 copy number gains, and 8 copy number losses. Consistent with prior studies, the most commonly mutated genes included TRAF3, FBXW7, POT1, TP53, SETD2, DDX3X and TBL1XR1. The most prominent copy number gain occurred on chromosome 13, overlapping key oncogenes such as MYC and KIT, while the most frequent deletion was a focal loss on chromosome 26, encompassing IGL, PRAME, GNAZ, RAB36, RSPH14, and ZNF280B. Notably, our set of recurrently mutated genes was significantly enriched with genes involved in epigenetic regulation. In particular, we identified hotspot mutations in two histone genes, H3C8, and LOC119877878, resulting in H3K27M alterations predicted to dysregulate gene expression. Finally, a survival analysis revealed that H3K27M mutations in H3C8 were associated with increased hazard ratios for progression-free survival. No copy number aberrations were associated with survival. These findings underscore the critical role of epigenetic dysregulation in cDLBCL and affirm the dog as a relevant large animal model for interrogating the biological activity of novel histone-modifying treatment strategies.

Women are worse off in developing and recovering from temporomandibular disorder symptoms

Abstract Decision-making for temporomandibular disorders (TMDs) is reported being a clinical challenge, partly due to uncertainities in assessment of long-term prognosis. Therefore, our aim was to explore variations over time in TMD symptoms and possible sex or age differences. In this cohort study, data were prospectively collected 2010–2017 from the general population in Västerbotten, Northern Sweden. Adults were eligible if they had undergone at least two routine dental check-ups that included screening for TMDs (3Q/TMD) from which states were defined as absence or presence of TMD pain and/or jaw catching/locking. The rate of transitions was estimated between TMD states within a time span of one year. A total of 94,769 individuals were included (49.9% women) with 205,684 repeated visits and 9,006 state transitions recorded over the 8-year period. Compared to men, women had higher rates of transitions from no TMDs to any TMD symptoms. Furthermore, women had a lower rate of transition from TMD pain only to no TMDs. The finding of a poorer prognosis in women, as well as previously reported potential gender differences in pain perception and reporting, reinforces that gender differences should be accounted for in the treatment planning stage for patients with onset of TMDs.

The spatio-temporal trend of climate and characterization of drought in Borana Zone, Ethiopia

Performance evaluation and prediction of optimal operational conditions for a compact date seeds milling unit using feedforward neural networks

Abstract Date seed grinding remains a significant challenge limiting the utilization of this valuable agricultural by-product." In this study, a compact date seeds grinding unit was designed, tested, and evaluated. The machine has two primary: a pair of toothed cylinders and a hammer mill. The machine’s performance was assessed in terms of throughput, specific energy consumption, and mean particle size of the product. First, the cylindrical section was tested under various conditions, including cylinder rotational speed (150, 250, 350, and 450 rpm), feed gate opening size (30, 37.5, and 45 cm2), and the clearance between cylinders (0, 1, and 2 mm). The feedforward neural network (FNN) framework predicated the optimal operating conditions for this part, which were recorded as 150 rpm cylinder rotational speed, 45 cm2 feed gate opening, and 2 mm cylinder clearance. This optimal operational condition was utilized as the starting conditions for subsequent testing of the hammer mill section. Then, the hammer mill was tested with different hammer rotational speeds (1250, 1500, and 1750 rpm) and screen hole diameters (2, 4, and 6 mm) underneath the hammers. The FNN model was again employed to predicate the most suitable operating parameters for the grinding unit. The key results included the optimal operational parameters at 150 rpm cylinder rotational speed, 2 mm clearance, 45 cm2 feeding area, 1750 rpm hammer speed, and 6 mm screen hole diameter. That operational condition resulted in 30 kg/h for machine’s throughput, 49 kW h/ton specific energy consumption, and 2.14 mm mean product size. With FNN model accuracy R2 of 0.99974, demonstrating high prediction reliability. Meanwhile, the operating cost was 0.027 $/kg, suitable for small to medium-scale operations. The significance of these findings lies in the development of an efficient, versatile milling solution for date seeds and similar agricultural materials. This research pioneers the application of machine learning in optimizing date seed processing, potentially revolutionizing agricultural waste valorization and opening new avenues for sustainable resource utilization.

Simulation of multilevel magnetic data storage via domain wall nucleation

We present micromagnetic simulations of spin–orbit torque (SOT)-induced multistate magnetization switching in a ferromagnetic layer with perpendicular anisotropy, conducted without an external magnetic field. Four volatile states are excited by a constant current. Each volatile state, after the removal of the current and undergoing relaxation and stabilization, can transition into one of four stable nonvolatile states. Further analysis revealed that, by specifically controlling the amplitude and active/inactive intervals of a rectangular pulse, a volatile state can transition to a robust nonvolatile state, providing a viable approach for multilevel magnetic data storage. The resistance of each magnetic domain state is qualitatively calculated, and their differences make these multilevel states detectable for information reading.

Dark-field x-ray microscopy for 2D and 3D imaging of microstructural dynamics at the European x-ray free-electron laser

Dark field x-ray microscopy (DXFM) can visualize microstructural distortions in bulk crystals. Using the femtosecond x-ray pulses generated by x-ray free-electron lasers (XFELs), DFXM can achieve sub-μm spatial resolution and <100 fs time resolution simultaneously. In this paper, we demonstrate ultrafast DFXM measurements at the European XFEL to visualize an optically driven longitudinal strain wave propagating through a diamond single crystal. We also present two DFXM scanning modalities that are new to the XFEL sources: spatial 3D and 2D axial-strain scans with sub-μm spatial resolution. With this progress in XFEL-based DFXM, we discuss new opportunities to study multi-timescale spatiotemporal dynamics of microstructures.

Strength, deformation, and the fcc–hcp phase transition in condensed Kr and Xe to the 100 GPa pressure range

The rare gas solids exhibit systematic differences in crystal structure, phase transition conditions, bond strength, and other physical properties. The physical properties of heavy rare gas solids krypton and xenon are modified by the martensitic phase transition from face-centered cubic to hexagonal close packed structure over a broad pressure range. Crystal structure, strength, and plastic deformation of krypton and xenon have been investigated at 300 K using compression in the diamond-anvil cell with synchrotron angle-dispersive x-ray diffraction and complementary ruby fluorescence spectroscopy for Xe. Stacking faults indicative of the fcc–hcp phase transition are observed at pressures at and above 1.23 ± 0.05 and 1.9 ± 0.6 GPa in Kr and Xe, respectively. The transition remains incomplete in both solids to pressures greater than 100 GPa. Strength determined from stress measurements in Pt and ruby standards at pressures up to 111 GPa and complemented by observations of strain and texture measurements obtained by x-ray diffraction in the radial geometry to 100 GPa indicates similar or higher strength than Ar at all conditions, with significant stiffening at 15–20 GPa. Radial diffraction data reveal the persistence of broad highly textured fcc diffraction lines to 101 GPa in Xe, suggesting that the axial measurements may underestimate the metastable persistence of the fcc phase due to biased sampling of hcp crystallites resulting from preferred crystallite orientation. Kr and Xe are compared with He, Ne, and Ar for a systematic understanding of physical properties and phase equilibria of rare gas solids.

A perspective on soft matter molecular simulations: Deformation and flow at mesoscopic timescales

In Multiscale Materials Modeling, an enduring vision is to extract the molecular mechanisms governing a certain materials phenomenon of interest in order to predict how the phenomenon will behave at a later time. This goal of predictive simulation has been discussed about a decade ago as a materials research challenge, in the Mesoscale Science Frontier, MSS. To date, it continues to motivate a growing community of computational materials science and technology. Here, we consider several materials phenomena of interest, each well known in their specific areas of application, to note that while molecular dynamics simulation is arguably the most widely used method, MD results have limitations in predicting or explaining the behavior of the phenomenon. For the type of phenomena selected here, we believe that one can raise the issue of whether MD is an appropriate method of molecular simulation in the design and performance testing of complex materials. There exists an alternative to MD, the approach of meta-dynamics simulation based on energy landscape sampling and transition state theory. This approach is notable because it allows predictive molecular simulations over timescales considerably longer than the traditional MD. We are in the process of implementing an enhanced meta-dynamics approach aimed at identifying unknown defect mechanisms, making it particularly well-suited for investigating the deformation processes in engineering alloys at timescales relevant to laboratory measurements of component performance and durability assurance. Our motivation is that such simulation capabilities will find many materials-centric applications. One such application is known as plasma-materials interactions, PMI. In PMI, the phenomenon of nuclear irradiation damage has been a practical challenge, relevant to both nuclear fission and fusion power generation systems. For the present perspective, we will focus on the use of meta-dynamics simulations in collaboration with the research activities at an academic fusion research center.

Estimating depth-directional thermal conductivity profiles using neural network with dropout in frequency-domain thermoreflectance

Non-contact and non-destructive methods are essential for accurately determining the thermophysical properties necessary for the optimal thermal design of semiconductor devices and for assessing the properties of materials with varying crystallinity across their thickness. Among these methods, frequency-domain thermoreflectance (FDTR) stands out as an effective technique for evaluating the thermal characteristics of nano/microscale specimens. FDTR varies the thermal penetration depth by modifying the heating frequency, enabling a detailed analysis of the thermophysical properties at different depths. This study introduces a machine learning approach that employs FDTR to examine the thermal conductivity profile along the depth of a specimen. A neural network model incorporating dropout techniques was adapted to estimate the posterior probability distribution of depth-wise thermal conductivity. Analytical databases for both uniform and non-uniform thermal conductivity profiles were generated, and the machine learning model was trained using these databases. The effectiveness of the predictive model was confirmed through assessments of both uniform and non-uniform thermal conductivity profiles, achieving a coefficient of determination between 0.96 and 0.99. For uniform thermal conductivity, the method attained mean absolute percentage errors of 1.362% for thermal conductivity and 3.466% for thermal boundary conductance (compared to actual values in the analytically calculated database). In cases of non-uniform thermal conductivity, the prediction accuracy decreased, particularly near the sample's surface, primarily due to the limited availability of machine learning data at higher heating frequencies.

Complete mathematical theory of the jamming transition: A perspective

The jamming transition of frictionless athermal particles is a paradigm to understand the mechanics of amorphous materials at the atomic scale. Concepts related to the jamming transition and the mechanical response of jammed packings have cross-fertilized into other areas such as atomistic descriptions of the elasticity and plasticity of glasses. In this perspective article, the microscopic mathematical theory of the jamming transition is reviewed from first-principles. The starting point of the derivation is a microscopically reversible particle-bath Hamiltonian from which the governing equation of motion for the grains under an external deformation is derived. From this equation of motion, microscopic expressions are obtained for both the shear modulus and the viscosity as a function of the distance from the jamming transition (respectively, above and below the transition). Regarding the vanishing of the shear modulus at the unjamming transition, this theory, as originally demonstrated by Zaccone and Scossa-Romano [Phys. Rev. B 83, 184205 (2011)], is currently the only quantitative microscopic theory in parameter-free agreement with numerical simulations of O’Hern et al. [Phys. Rev. E 68, 011306 (2003)] for jammed packings. The divergence of the viscosity upon approaching the jamming transition from below is derived here, for the first time, from the same microscopic Hamiltonian. The quantitative microscopic prediction of the diverging viscosity is shown to be in fair agreement with numerical results of sheared 2D soft disks from Olsson and Teitel [Phys. Rev. Lett. 99, 178001 (2007)].

Study and optimization on hyperthermia performance of magnetic fluids modeled by coupled Brownian–Néel rotations

Superparamagnetic nanoparticles (SMNPs) with nonlinear magnetic behavior under alternating magnetic fields hold great potential in biomedical applications, including magnetic hyperthermia, biosensing, magnetic separation, magnetic particle imaging, etc. Magnetic hyperthermia therapy, based on the relaxation movement of SMNPs subjected to an alternating magnetic field, is a promising modality for tumor treatment. Herein, we applied the stochastic Langevin equation to study the magnetic hyperthermia performance of SMNPs by taking the coupled Brownian–Néel rotations into account. The specific absorption rate (SAR) of magnetic fluids is used as a parameter for optimizing the amplitude and frequency of alternating magnetic fields and guiding the design of SMNPs. Specifically, by accounting for the dipole–dipole interactions between SMNPs, it is revealed that these interactions significantly suppress the relaxation behavior of particles, thereby reducing SAR. Furthermore, this study systematically examines the effects of key factors such as particle concentration, particle size, and magnetic anisotropy constant on SAR. The accurate prediction of the rotational and magnetic dynamics of SMNPs under an oscillating magnetic field provides valuable theoretical insights and technical support for the optimized design of external magnetic field systems and the precise fabrication of SMNPs.

Modeling of shock wave loading FeO to 1000 GPa

Iron oxide, FeO, is one of the main rock-forming oxides. Research into its thermophysical properties under high-energy loading is necessary to construct an equation of state that is used in modeling the properties of Earth's mantle and core as well as other celestial bodies. The results of calculations of thermodynamic properties of FeO under shock compression up to 1000 GPa are presented. In the phase transition field, calculations for FeO are performed as a mixture of low- and high-pressure phases based on the assumption that components of the mixture are in thermodynamic equilibrium under shock wave loadings. The conditions at the wave front are expressed in Rankin–Hugoniot ratios that express conservation of mass, momentum, and energy. Conservation conditions for momentum and energy flow are written for the mixture overall, while conservation conditions for mass flow are written separately for each component. Supplementing the obtained expressions with the condition of equality of the component temperature values and the equations of state for each component, shock adiabatic curves for a heterogeneous material are obtained. This method allows us to accurately describe the shock-wave loading of FeO, including in the phase transition region. Verification of simulation results is carried out using data obtained from experiments and calculations by other researchers. The considered technique is useful for calculations of similarly complex materials.

First-principles study on segregation anisotropy of grain boundaries in Pt–Au alloys

Gold (Au) segregation at Pt grain boundaries (GBs) plays an important role in the properties of Pt-based alloys. It was reported that close-packed GBs and open GBs exhibit different segregation behaviors, and their origin is still unclear. Based on the density functional theory as implemented in the exact muffin-tin orbitals method and the full charge density technique, we investigate the impact of bulk composition and temperature on the segregation behaviors of the Σ3(111)[11¯0], Σ5(310)[001], and Σ9(221)[11¯0] symmetric tilt GBs in Pt–Au alloys. It is revealed that the segregation driving forces are correlated with the large local volume near the GB and the miscibility gap in Pt–Au alloys. At finite temperatures when the configurational entropy is considered, a competition between the chemical driving force and the configurational entropy is responsible for the segregation anisotropy in Pt–Au alloys. The bulk composition has a small effect on the segregation energy but strongly impacts the equilibrium concentration profiles at finite temperatures. The present study provides a theoretical analysis for the segregation anisotropy, and the methodology utilized in this work can be generalized to other binary or multi-component dilute or concentrated alloys while the composition variation is involved.