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Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO2. Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. The robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

Lithium-ion batteries are the leading energy storage technology for portable electronics and vehicle electrification. However, demands for enhanced energy density, safety, and scalability necessitate solid-state alternatives to traditional liquid electrolytes. Moreover, the rapidly increasing utilization of lithium-ion batteries further requires that next-generation electrolytes are derived from earth-abundant raw materials in order to minimize supply chain and environmental concerns. Toward these ends, clay-based nanocomposite electrolytes hold significant promise since they utilize earth-abundant materials that possess superlative mechanical, thermal, and electrochemical stability, which suggests their compatibility with energy-dense lithium metal anodes. Despite these advantages, nanocomposite electrolytes rarely employ kaolinite, the most abundant variety of clay, due to strong interlayer interactions that have historically precluded efficient exfoliation of kaolinite. Overcoming this limitation, here we demonstrate a scalable liquid-phase exfoliation process that produces kaolinite nanoplatelets (KNPs) with high gravimetric surface area, thus enabling the formation of mechanically robust nanocomposites. In particular, KNPs are combined with a succinonitrile (SN) liquid electrolyte to form a nanocomposite gel electrolyte with high room-temperature ionic conductivity (1 mS cm–1), stiff storage modulus (>10 MPa), wide electrochemical stability window (4.5 V vs Li/Li+), and excellent thermal stability (>100 °C). The resulting KNP-SN nanocomposite gel electrolyte is shown to be suitable for high-rate rechargeable lithium metal batteries that employ high-voltage LiNi0.8Co0.15Al0.05O2(NCA) cathodes. While the primary focus here is on solid-state batteries, our strategy for kaolinite liquid-phase exfoliation can serve as a scalable manufacturing platform for a wide variety of other kaolinite-based nanocomposite applications.

Hydrogen (H2) is a clean alternative to fossil fuels and can be produced by using renewable energy sources. However, due to its low volumetric energy density, H2storage requires low temperatures and/or high pressures for practical applications. H2storage using metal–organic frameworks (MOFs) has gained traction due to the large surface areas and highly tunable nature of MOFs, but suitable materials for room-temperature storage remain elusive. In this study, we performed ab initio calculations to study H2storage at room temperature in two anionic MOFs (NOTT-200 and SU-102) and examined the effects of different exchanged metal cations (Li+, Na+, K+, Mg2+, and Ca2+) on hydrogen adsorption. Our results show that the H2adsorption enthalpy on Mg2+cations is the highest among the cations studied, with average adsorption enthalpies of −18.7 and −23.3 kJ/mol at three H2molecules per Mg2+in NOTT-200 and SU-102, respectively. These adsorption enthalpies are in the desired range for room temperature H2storage. We further estimated the volumetric H2uptake for 2,058 anionic MOFs from the Cambridge Structural Database using assumptions based on our modeling and found that 459 MOFs could potentially exhibit a H2volumetric uptake of more than 15.0 g/L. We synthesized NOTT-200-Mg, a magnesium-exchanged version of NOTT-200, and measured the H2heat of adsorption experimentally. The measured H2heat of adsorption was significantly lower than the results from density functional theory (DFT). Further DFT calculations show that water molecules can bind strongly to the Mg2+cation and thus reduce the H2heat of adsorption.

Introduction We assessed trends in HIV and syphilis prevalence, HIV incidence, related risk factors, and preventive behaviors among men who have sex with men (MSM) in Vietnam from 2015 to 2020.Methods Data originated from the HIV Sentinel Surveillance Plus system, which sampled MSM at venues and hotspots in seven of Vietnam’s 63 provinces in 2015, 2016, 2018, and 2020 (N= 1100-1445 per year; ∼150-300 per province per year).Results HIV prevalence estimates increased from 6.6% (95% CI 4.5–9.6) in 2015 to 13.8% (95% CI 10.5-18.2, p= .001 for trend) in 2020 overall, and separately in An Giang, Can Tho, Hai Phong, and Khanh Hoa provinces but not in Ho Chi Minh City, Hanoi, or Kien Giang. Syphilis prevalence increased from 2.7% (95% CI 1.4-5.1) in 2015 to 12.6% (95% CI 8.7-18.0) in 2020 overall (p< .001 for trend), and separately in An Giang, Can Tho, and Hai Phong provinces but not in Ho Chi Minh City or Kien Giang. We calculated time-at-risk from first anal sex to first HIV-positive or last HIV-negative test to estimate HIV incidence. Estimated HIV incidence suggested increasing rates of seroconversion from 1.36 per 100 person-years experienced by participants in 2015 to 2.61 per 100 person-years among participants in 2020 (hazard ratio per year 1.13, 95% CI 1.08-1.18, p< .001). There was a statistically significant increase in HIV testing, STI testing, and receipt of free condoms over the period (p< .05 for trend), and a statistically significant decrease in amphetamine use (p= .043 for trend).Conclusions Despite prevention efforts and improvements in some risk indicators, consecutive cross-sectional sampling results provide evidence of increasing incidence of HIV and syphilis among MSM in Vietnam, especially outside the major cities. Aggressive HIV prevention and treatment services can be expanded while conducting deeper investigations into the causes of these increases.

Platinum (Pt) thin films are useful in applications requiring high-conductivity electrodes with excellent thermal and chemical stability. Ultrasmooth and epitaxial Pt thin films with single-crystalline domains have the added benefit of providing ideal templates for the subsequent growth of heteroepitaxial structures. Here, we grow epitaxial Pt (111) electrodes (ca. 30 nm thick) on sapphire (α-Al2O3(0001)) substrates with pulsed laser deposition. This versatile technique allows control of the growth process and fabrication of films with carefully tailored parameters. X-ray scattering, atomic-force microscopy, and electron microscopy provide structural characterization of the films. Various gaseous atmospheres and temperatures were explored to achieve epitaxial growth of films with low roughness. A two-step (500 °C/300 °C) growth process was developed, yielding films with improved epitaxy without compromising roughness. The resulting films possess ultrasmooth interfaces (<3 Å) and high electrical conductivity (6.9 × 106S/m). Finally, Pt films were used as current collectors and templates to grow lithium manganese oxide (LiMn2O4(111)) epitaxial thin films, a cathode material used in Li-ion batteries. Using a solid-state ionogel electrolyte, the films were highly stable when electrochemically cycled in the 3.5–4.3 V vs Li/Li+range.

The CRYSTALS-Dilithium protocol is considered as one of the most promising digital signature schemes in NIST’s post-quantum cryptography standardization process. While separating arithmetic computation units can be advantageous in some cases, it can lead to increased hardware resource consumption and performance degradation. To overcome this issue, this paper proposes a novel architecture called the Unified Polynomial Arithmetic Unit (UniPAU), specifically designed for the Dilithium signature scheme. The proposed UniPAU offers a unique hardware module that can execute all the polynomial operations required for the Dilithium signature scheme. To demonstrate the effectiveness of our design, we implemented it on the Xilinx Zynq UltraScale+ ZCU102 (xczu9eg-ffvb1156-2-e) FPGA platform and evaluated its hardware efficiency and performance. Our implementation results indicate that the proposed UniPAU can achieve comparable throughput while consuming fewer hardware resources compared to state-of-the-art studies. These findings suggest that our UniPAU can provide an optimized and efficient hardware solution for polynomial arithmetic operations in the Dilithium signature scheme.

Entropy-engineered materials are garnering considerable attention owing to their excellent mechanical and transport properties, such as their high thermoelectric performance. However, understanding the effect of entropy on thermoelectrics remains a challenge. In this study, we used the PbGeSnCdxTe3+xfamily as a model system to systematically investigate the impact of entropy engineering on its crystal structure, microstructure evolution, and transport behavior. We observed that PbGeSnTe3crystallizes in a rhombohedral structure at room temperature with complex domain structures and transforms into a high-temperature cubic structure at ∼373 K. By alloying CdTe with PbGeSnTe3, the increased configurational entropy lowers the phase-transition temperature and stabilizes PbGeSnCdxTe3+xin the cubic structure at room temperature, and the domain structures vanish accordingly. The high-entropy effect results in increased atomic disorder and consequently a low lattice thermal conductivity of 0.76 W m–1K–1in the material owing to enhanced phonon scattering. Notably, the increased crystal symmetry is conducive to band convergence, which results in a high-power factor of 22.4 μW cm–1K–1. As a collective consequence of these factors, a maximum ZTof 1.63 at 875 K and an average ZTof 1.02 in the temperature range of 300–875 K were obtained for PbGeSnCd0.08Te3.08. This study highlights that the high-entropy effect can induce a complex microstructure and band structure evolution in materials, which offers a new route for the search for high-performance thermoelectrics in entropy-engineered materials.

Let ϕ(x,y):ℝd×ℝd→ℝbe a function. We say ϕis a Mattila–Sjölin type functionof index γif γis the smallest number satisfying the property that for any compact set E⊂ℝd, ϕ(E,E) has a non-empty interior whenever dimH(E)>γ. The usual distance function, ϕ(x,y) = |x− y|, is conjectured to be a Mattila–Sjölin type function of index d2. In the setting of finite fields Fq, this definition is equivalent to the statement that ϕ(E,E)=Fqwhenever |E|≫ qγ. The main purpose of this paper is to prove the existence of such functions with index d2in the vector space Fqd.

Limitations on hardware resource consumption and throughput make the use of non-binary low-density parity-check (NB-LDPC) codes challenging in practical applications. This brief first proposes a novel check-node (CN) decoding algorithm called Hamming-distance trellis min-max (H-TMM) to reduce the complexity introduced by searching for two minimum values in previous TMM-based algorithms. Then, by taking advantage of the proposed algorithm and the number appearances of reliable values, a high-performance H-TMM-based NB-LDPC decoder architecture is presented. Experiments on the 32-ary (837, 726) NB-LDPC code confirmed that the proposed decoder can obtain a high throughput on less hardware resources compared with the state-of-the-art works while maintaining a competitive error-correcting performance. In particular, the proposed CN unit architecture reduced hardware resources by almost half compared to that in the latest decoder.

With millions of optical links deployed in a typical Data Center (DC) network, telemetry is becoming increasingly important, especially as the number of links and transmission bandwidths rise over time. Multi-path interference (MPI) arising out of multiple optical reflections from fiber connectors, transmitters and receivers is one optical impairment that has been known to cause performance degradation of links in the DC. Through monitoring the statistics of each pulse amplitude modulation (PAM) level at the receiver, we propose and experimentally demonstrate a digital signal processing (DSP) based method to detect the presence of a high level of MPI in intensity modulation direct detection (IMDD) networks. Having this information at hand, the fiber path can be cleaned before an optical link is degraded or fails, which can prevent misdiagnosis and can save time and effort spent on triaging after failure.

Let Abe a compact set in R$\mathbb {R}$, and E=Ad⊂Rd$E=A^d\subset \mathbb {R}^d$. We know from the Mattila–Sjölin's theorem if dimH(A)>d+12d$\dim _H(A)>\frac{d+1}{2d}$, then the distance set Δ(E)$\Delta (E)$has non‐empty interior. In this paper, we show that the threshold d+12d$\frac{d+1}{2d}$can be improved whenever d⩾5$d\geqslant 5$.

•What is the primary question addressed by this study?The current paper examines exploratory analyses of outcomes of the culturally adapted, relatively brief, multicomponent family dementia caregiver intervention, Resources for Enhancing Alzheimer's Caregiver Health in Vietnam (REACH VN).•What is the main finding of this study?There was significant improvement in caregiver frustrations post-intervention in the REACH VN group compared with enhanced control. Additionally, there were differences in other outcomes (e.g., care recipient problem behaviors and associated caregiver bother) favoring the intervention condition that did not reach statistical significance.•What is the meaning of the finding?This paper provides further evidence that REACH VN is a promising intervention to support caregivers of people with dementia in Vietnam.

A 3D DEM model using Particle Flow Code (PFC3D) software was developed utilizing a bonded-ball flexible membrane approach to study cohesionless soil as a discontinuous discrete material. This approach is not yet widely used because of its complexity and high computational cost, but it allowed the authors to observe the stress-strain curves of triaxial specimens, to single out effects of individual factors on the strength and strain properties, and to observe the formation of the shear band and failure surface. The 3D model was calibrated and verified with experimental data, and a sensitivity analysis was carried out for the microparameters. Triaxial tests were simulated to observe the stress-strain curves and volumetric changes, as well as the strength parameters of soils consisting of spherical particles with different gradations but the same porosity. The authors investigated the effects of mean particle size, larger particle size, smaller particle size, and soil gradation on three soil parameters: peak deviatoric stress, internal friction angle, and dilatancy angle. Four different cases with different soil gradations and particle sizes were studied: a uniform soil, a soil with randomly created particle sizes, and two soils each contains two particle sizes. For two out of the four cases studied, peak deviatoric stress, internal friction angle, and dilatancy angle increased when the mean particle size D50increased. For the other two cases, the parameters decreased when the mean particle size D50increased. One important finding is that the relationships between particle size and deviatoric stress, internal friction angle, and dilatancy angle were found to be linear. These relationships can provide predictions on soil strength and strain properties when the particle size changes. Observations and discussions on the formation of shear bands during shear testing are also presented. A step-by-step delineation of the DEM model development is also presented with the development process of a flexible membrane carefully described.

Control of nucleation sites is an important goal in materials growth: nuclei in regular arrays may show emergent photonic or electronic behavior, and once the nuclei coalesce into thin films, the nucleation density influences parameters such as surface roughness, stress, and grain boundary structure. Tailoring substrate properties to control nucleation is therefore a powerful tool for designing functional thin films and nanomaterials. Here, we examine nucleation control for metals deposited on two-dimensional materials in a situation where substrate effects are absent and heterogeneous nucleation sites are minimized. Through quantification of faceted, epitaxial Au island nucleation on graphene, we show that ultralow nucleation densities with nuclei several micrometers apart can be achieved on suspended graphene under conditions where we measure 2–3 orders of magnitude higher nucleation density on the adjacent supported substrate. We estimate diffusion distances using nucleation theory and find a strong sensitivity of nucleation and diffusion to suspended graphene thickness. Finally, we discuss the role of surface roughness as the main factor determining nucleation density on clean free-standing graphene.

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To characterize the entire rat corneal nerve architecture, the changes that occur with aging,and its sensory, sympathetic, and parasympathetic fiber distribution.

Vision-based fire detection is widely studied recently to reduce the damage of fire disaster thanks to the advantages of software-based methods comparing to traditional hardware-based fire detection using sensors. This paper presents a novel method for fire detection using the convolutional neural networks on image sequences of videos to extract both the spatial and temporal information for fire classification. The system includes a CNN network to extract the image features, and short-term and long-term stages at the end for classification. Experiments carried out on the common public datasets show promising results in terms of performance in comparison to the previous works.

We introduce a graphene-based nanofluidic cell that facilitates in situimaging of liquid samples via transmission electron microscopy. The cell combines the benefits of graphene liquid cells—namely, high resolution, reduced charging effects, and excellent sample stability—with the ability to introduce reactants and control fluid concentrations as provided by conventional silicon-nitride-windowed flow cells. The graphene flow cell offers significantly less window bowing compared to existing commercial holders. We demonstrate the performance of the flow cell by imaging gold nanoparticle dynamics and uranyl acetate crystallization. Our results confirm the utility of graphene flow cells in obtaining the high spatial and temporal resolution required for probing the complex dynamics of nanoparticles and nucleation pathways in aqueous solutions.

Polarons are localized electronic states that occur in many semiconductors. Modeling polarons at the quantum or atomic scale is often performed using electronic structure methods such as density functional theory (DFT). A problem using DFT to model polarons is that self-interaction errors (SIEs) often result in delocalized electronic states rather than localized states. Methods such as DFT + Uor hybrid functionals can be used to overcome SIE, but these methods may still not form stable polarons. The initial geometries and wavefunctions strongly influence and determine how and if polarons may arise during electronic structure calculations. In this paper, we have assessed different strategies to efficiently obtain low-energy localized polarons in several semiconductors (TiO2, m-HfO2, and m-BiVO4). These strategies involve distorting the initial geometry to create polaron-like geometries or generating initial wavefunctions that mimic polaronic states. We show that perturbing the crystal’s structure to induce polaron formation (which we call the bond distortion method) is a very efficient approach to form stable polarons, requiring less computational time than other methods. In contrast, other methods that we assessed may not lead to stable polaron states or may require much greater time (up to four times more computational time). Having a reliable, efficient method to ensure polaron formation is crucial to modeling polarons. The results described herein will save wasted computational efforts and also enable efforts such as high-throughput simulation of polarons.

Phosphotyrosine (pY)-specific phosphoproteome profiles have been determined for 16 AML cell lines through a pY immunoprecipitation-based protocol. Kinase activity inference using dedicated ranking analysis, combining kinome-, activation loop-, and substrate-based analyses, identified potential drivers for all cell lines and driver function was confirmed through drug experiments. Results for two patient samples show potential of phosphoproteomics in a clinical setting.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring(PRM), and massively parallel data-independent acquisition(DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

•What is the primary question addressed by this study?This is a two-armed cluster randomized controlled trial to test the efficacy of a culturally adapted family caregiver intervention (REACH VN) in reducing caregiver psychological distress and caregiver burden in Vietnam.•What is the main finding of this study?Caregivers in the REACH VN intervention group had significantly lower psychological distress at 3 months and caregiving burden at 6 months compared to those in the control group. Secondary outcomes (perceived stress, somatic symptoms) were also significantly improved at 3 months but not at 6 months.•What is the meaning of the finding?REACH VN, a multicomponent intervention, is efficacious in supporting and improving the health of family dementia caregivers in Vietnam although further modifications may be needed to sustain effects after 3 months.

to identify the efficacy and tolerability of Proteoglycan F in patients with primary knee OA. Design: A 24-week randomized, placebo-controlled, double-blind clinical trial with two arms: (1) Proteoglycan F (received 10 mg proteoglycan daily, for 24 weeks) and (2) control group (received placebo). Knee symptoms and joint cartilage status (evaluated by ultrasound and MRI of knee joints), quality of life, serum cytokine levels (IL-1β and TNF-α), and safety evaluation were measured before, during, and after the treatment.

Global phosphoproteomics on human testicular tissue was performed by using highly efficient titanium dioxide (TiO2) method coupled to LC-MS/MS. Mass spectrometry data were used to predict the most prominent kinases, and phosphoregulation was revealed as highly active during spermatogenesis. Protein kinases are good targets for pharmacological intervention in male fertility and testicular cancer progression. In fact, CDK12 and PAK4 were found to be potentially involved in the formation of sperm structures and cancer cell response to apoptosis, respectively.

Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry–based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient–derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.

When building Soil Geosynthetic Composite (SGC) walls, fill compaction is normally carried out by operating a compactor in a general direction parallel to the wall face. In other words, a moving point or area load is often used to apply a compaction load on a newly installed soil lift. Pham (2009) and Wu and Pham (2010) demonstrated that the compaction-induced stress (CIS) caused by multiple passes of a compactor moving toward or away from a section can be calculated by taking into account the compaction load applied directly above the section under consideration using a simplified stress path proposed by Duncan and Seed (1986). Additionally, by simulating the compaction, the CIS due to fill compaction may be correctly assessed. The CIS resulting from fill compaction can also be accurately assessed by simulating the compaction load, such as by applying a distribution load on top of each backfill layer or a distribution load at the top and bottom of each soil layer, or by applying various widths of strip load to the top of each backfill layer. The objective of this study was to validate the numerical simulation of the compaction load to stress deformation behavior of SGC mass under operating stress conditions. In order to conduct the numerical analysis, data from both a full-scale instrumented SGC mass based on large-scale soil geosynthetic composite (SGC) experiments and a 6 m-high SGC (Pham, 2009) were employed. This study will examine a few SGC behavior parameters, including reinforcement strains, lateral displacements, and reinforcement strains. The objective of the FE modeling is to demonstrate the effect, emphasize the significance of the compaction conditions to the stress-deformation behavior of SGC mass, and validate the findings from the field-scale experiments and proposed model by Pham (2009) and Wu and Pham (2010).

Supported single metal atoms embody active, efficient catalysts. Much effort in the literature has focused on late transition metals, but understanding the reactivity and stability trends across all metals is key to developing new metal catalysts. Metal–support interactions are especially important since they determine stability. We used density functional theory (DFT) to model binding of all 29 transition metal atoms and 8 post-transition metal atoms to TiO2, a prototypical support. The binding energies ranged from very strong (up to −7.6 eV) for early transition metals to weak (>−1 eV) for late transition metals. Adsorbed early transition metals were strongly cationic while adsorbed later transition metals were slightly cationic. Stability calculations (aggregation, diffusion, and substitution) indicated that several metal atoms were stable on the surface. Statistical models demonstrate that metal–oxygen bond dissociation energies correlate well with the binding of the metal atoms to the surface, while statistical models using other parameters (electronegativity or group number) also predict metal adsorption energies well. Gap states consisted of predominantly TiO2states for early transition metals and of predominantly metal adatom states for mid/late transition metals. To understand the reactivity trends, we modeled activation of CO2, or adsorption of bent CO2, which is an important initial step in the CO2reduction reaction. We found that inexpensive and abundant early/mid transition metals could activate CO2strongly. Using statistical learning methods, we also identified several descriptors that may explain the CO2adsorption energies, such as workfunction, cohesive energy, and d-band center of metal adatom. We also briefly discuss CO2dissociation calculations over M/TiO2catalysts. Our work furthers understanding of single atom and atomically dispersed catalysts and provides motivation to synthesize and study a wider class of catalysts, including early and mid transition metals.

The heat generated from electronic devices such as light emitting diodes (LEDs), batteries, and highly integrated transistors is one of the major causes obstructing the improvement of their performance and reliability. Herein, we report a comprehensive method to dissipate the generated heat to a vast area by using the new type of graphene–carbon–metal composite film as a heat sink. The unique porous graphene–carbon–metal composite film that consists of an electrospun carbon nanofiber with arc-graphene (Arc-G) fillers and an electrochemically deposited copper (Cu) layer showed not only high electrical and thermal conductivity but also high mechanical stability. Accordingly, superior thermal management of LED devices to that of conventional Cu plates and excellent resistance stability during the repeated 10 000 bending cycles has been achieved. The heat dissipation of LEDs has been enhanced by the high heat conduction in the composite film, heat convection in the air flow, and thermal radiation at low temperature in the porous carbon structure. This result reveals that the graphene–carbon–metal composite film is one of the most promising materials for a heat sink of electronic devices in modern electronics.

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.

Proteogenomics, i.e.comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated downmodulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared with their controls. Splice variants identified included RAC1, OSBPL3, MKI67, and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 downmodulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g.allowing to identify clinically relevant biomarkers.

Organic–inorganic halide perovskite materials have emerged as attractive alternatives to conventional solar cell building blocks. Their high light absorption coefficients and long diffusion lengths suggest high power conversion efficiencies, and indeed perovskite-based single bandgap and tandem solar cell designs have yielded impressive performances. One approach to further enhance solar spectrum utilization is the graded bandgap, but this has not been previously achieved for perovskites. In this study, we demonstrate graded bandgap perovskite solar cells with steady-state conversion efficiencies averaging 18.4%, with a best of 21.7%, all without reflective coatings. An analysis of the experimental data yields high fill factors of ∼75% and high short-circuit current densities up to 42.1 mA cm−2. The cells are based on an architecture of two perovskite layers (CH3NH3SnI3and CH3NH3PbI3−xBrx), incorporating GaN, monolayer hexagonal boron nitride, and graphene aerogel.

A simple and versatile strategy is presented for the localized on-chip synthesis of an ordered metal oxide hollow sphere array directly on a low power microheater platform to form a closely integrated miniaturized gas sensor. Selective microheater surface modification through fluorinated monolayer self-assembly and its subsequent microheater-induced thermal decomposition enables the position-controlled deposition of an ordered two-dimensional colloidal sphere array, which serves as a sacrificial template for metal oxide growth via homogeneous chemical precipitation; this strategy ensures control in both the morphology and placement of the sensing material on only the active heated area of the microheater platform, providing a major advantage over other methods of presynthesized nanomaterial integration via suspension coating or printing. A fabricated tin oxide hollow sphere-based sensor shows high sensitivity (6.5 ppb detection limit) and selectivity toward formaldehyde, and extremely fast response (1.8 s) and recovery (5.4 s) times. This flexible and scalable method can be used to fabricate high performance miniaturized gas sensors with a variety of hollow nanostructured metal oxides for a range of applications, including combining multiple metal oxides for superior sensitivity and tunable selectivity.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a readout of aberrant signaling and has the potential to identify new targets and guide treatment decisions. Using two-step sequential phosphopeptide enrichment, we generate a comprehensive phosphoproteome and proteome of nine PDAC cell lines, encompassing more than 20,000 phosphosites on 5,763 phospho-proteins, including 316 protein kinases. By using integrative inferred kinase activity (INKA) scoring, we identify multiple (parallel) activated kinases that are subsequently matched to kinase inhibitors. Compared with high-dose single-drug treatments, INKA-tailored low-dose 3-drug combinations against multiple targets demonstrate superior efficacy against PDAC cell lines, organoid cultures, and patient-derived xenografts. Overall, this approach is particularly more effective against the aggressive mesenchymal PDAC model compared with the epithelial model in both preclinical settings and may contribute to improved treatment outcomes in PDAC patients.

ABSTRACTBackgroundScreening for colorectal cancer (CRC) reduces its mortality but has limited sensitivity and specificity. Aims We aimed to explore potential biomarker panels for CRC and adenoma detection and to gain insight into the interaction between gut microbiota and human metabolism in the presence of these lesions.MethodsThis multicenter case-control cohort was performed between February 2016 and November 2019. Consecutive patients ≥18 years with a scheduled colonoscopy were asked to participate and divided into three age, gender, body-mass index and smoking status-matched subgroups: CRC (n = 12), adenomas (n = 21) and controls (n = 20). Participants collected fecal samples prior to bowel preparation on which proteome (LC-MS/MS), microbiota (16S rRNA profiling) and amino acid (HPLC) composition were assessed. Best predictive markers were combined to create diagnostic biomarker panels. Pearson correlation-based analysis on selected markers was performed to create networks of all platforms.ResultsCombining omics platforms provided new panels which outperformed hemoglobin in this cohort, currently used for screening (AUC 0.98, 0.95 and 0.87 for CRC vs controls, adenoma vs controls and CRC vs adenoma, respectively). Integration of data sets revealed markers associated with increased blood excretion, stress- and inflammatory responses and pointed toward downregulation of epithelial integrity.ConclusionsIntegrating fecal microbiota, proteome and amino acids platforms provides for new biomarker panels that may improve noninvasive screening for adenomas and CRC, and may subsequently lead to lower incidence and mortality of colon cancer.

Expression of apoptosis-regulating proteins (B-cell CLL/lymphoma 2 - BCL-2, Myeloid Cell Leukemia 1 - MCL-1, BCL-2 like 1 - BCL-X and BCL-2-associated X protein - BAX) in acute myeloid leukemia (AML) blasts at diagnosis is associated with disease-free survival. We previously found that the initially high apoptosis-resistance of AML cells decreased after therapy, while regaining high levels at relapse. Herein, we further explored this aspect of dynamic apoptosis regulation in AML. First, we showed that the intraindividual ex vivoapoptosis-related profiles of normal lymphocytes and AML blasts within the bone marrow of AML patients were highly correlated. The expression values of apoptosis-regulating proteins were far beyond healthy control lymphocytes, which implicates the influence of microenvironmental factors. Second, we demonstrated that apoptosis-resistant primary AML blasts, as opposed to apoptosis-sensitive cells, were able to up-regulate BCL-2 expression in sensitive AML blasts in contact cultures (p= 0.0067 and p= 1.0, respectively). Using secretome proteomics, we identified novel proteins possibly engaged in apoptosis regulation. Intriguingly, this analysis revealed that major functional protein clusters engaged in global gene regulation, including mRNA splicing, protein translation, and chromatin remodeling, were more abundant (p= 4.01E-06) in secretomes of apoptosis-resistant AML. These findings were confirmed by subsequent extracellular vesicle proteomics. Finally, confocal-microscopy-based colocalization studies show that splicing factors-containing vesicles secreted by high AAI cells are taken up by low AAI cells. The current results constitute the first comprehensive analysis of proteins released by apoptosis-resistant and sensitive primary AML cells. Together, the data point to vesicle-mediated release of global gene regulatory protein clusters as a plausible novel mechanism of induction of apoptosis resistance. Deciphering the modes of communication between apoptosis-resistant blasts may in perspective lead to the discovery of prognostic tools and development of novel therapeutic interventions, aimed at limiting or overcoming therapy resistance.

In Birt–Hogg–Dubé (BHD) syndrome, germline loss-of-function mutations in the Folliculin (FLCN) gene lead to an increased risk of renal cancer. To address how FLCN inactivation affects cellular kinase signaling pathways, we analyzed comprehensive phosphoproteomic profiles of FLCNPOSand FLCNNEGhuman renal tubular epithelial cells (RPTEC/TERT1). In total, 15,744 phosphorylated peptides were identified from 4329 phosphorylated proteins. INKA analysis revealed that FLCN loss alters the activity of numerous kinases, including tyrosine kinases EGFR, MET, and the Ephrin receptor subfamily (EPHA2 and EPHB1), as well their downstream targets MAPK1/3. Validation experiments in the BHD renal tumor cell line UOK257 confirmed that FLCN loss contributes to enhanced MAPK1/3 and downstream RPS6K1/3 signaling. The clinically available MAPK inhibitor Ulixertinib showed enhanced toxicity in FLCNNEGcells. Interestingly, FLCN inactivation induced the phosphorylation of PIK3CD (Tyr524) without altering the phosphorylation of canonical Akt1/Akt2/mTOR/EIF4EBP1 phosphosites. Also, we identified that FLCN inactivation resulted in dephosphorylation of TFEB Ser109, Ser114, and Ser122, which may be linked to increased oxidative stress levels in FLCNNEGcells. Together, our study highlights differential phosphorylation of specific kinases and substrates in FLCNNEGrenal cells. This provides insight into BHD-associated renal tumorigenesis and may point to several novel candidates for targeted therapies.

Aerogels are used in a broad range of scientific and industrial applications due to their large surface areas, ultrafine pore sizes, and extremely low densities. Recently, a large number of reports have described graphene aerogels based on the reduction of graphene oxide (GO). Though these GO-based aerogels represent a considerable advance relative to traditional carbon aerogels, they remain significantly inferior to individual graphene sheets due to their poor crystallinity. Here, we report a straightforward method to synthesize highly crystalline GO-based graphene aerogels viahigh-temperature processing common in commercial graphite production. The crystallization of the graphene aerogels versusannealing temperature is characterized using Raman and X-ray absorption spectroscopy, X-ray diffraction, and electron microscopy. Nitrogen porosimetry shows that the highly crystalline graphene macrostructure maintains a high surface area and ultrafine pore size. Because of their enhanced crystallinity, these graphene aerogels exhibit a ∼200 °C improvement in oxidation temperature and an order of magnitude increase in electrical conductivity.

The presence of geosynthetic reinforcement in soil mass has been shown to reduce the tendency of dilation of the soil when subject to shear stress, especially when the reinforcement is closely spaced. As tensile loads are induced in geosynthetic reinforcement, adjacent reinforcement layers tend to act as tensioned membranes that inhibit dilation of the soil enclosedbetween the reinforcement layers. The suppression of soil dilation leads to stronger soil and is regarded as a reinforcing mechanism. This paper presents for the first time measured volume change behavior of field-scaleexperiments on reinforced and unreinforced soils. The volume change behavior, as characterized quantitatively by the angle of dilation, is presented and discussed. This paper also describes finite element analysis for the stress–strain and volume change behavior of field-scale soil-geosynthetic composites. Using the calibrated finite element model, a parametric study was conducted to examine the effects of reinforcement spacing, reinforcement stiffness, and soil stiffness on volume change behavior of soil-geosynthetic composites. The measured data and finite element analysis results suggest that the mechanism of suppression of soil dilation (addresses volume change behavior), and the mechanisms of apparent confining pressure and apparent cohesion (two popular mechanisms that address strength behavior) are likely independent.

We study the application of the receding horizon optimal control (RHOC) for hydraulic pipeline systems described by the so-called water hammer equations. Sufficient conditions to guarantee an asymptotic stability to an equilibrium state are first introduced and then integrated in the RHOC scheme. For the implementation, calculus of variations is employed to characterize the optimal solution in terms of the adjoint state and the recently proposed Lattice Boltzmann method is used to solve both direct and adjoint partial differential equations. This approach is finally validated in simulation. [ABSTRACT FROM AUTHOR]

Introduction

In contrast to various signatures that predict the prognosis of breast cancer patients, markers that predict chemotherapy response are still elusive. To detect such predictive biomarkers, we investigated early changes in protein expression using two mouse models for distinct breast cancer subtypes who have a differential knock-out status for the breast cancer 1, early onset (Brca1) gene. The proteome of cisplatin-sensitive BRCA1-deficient mammary tumors was compared with that of cisplatin-resistant mammary tumors resembling pleomorphic invasive lobular carcinoma. The analyses were performed 24 h after administration of the maximum tolerable dose of cisplatin. At this time point, drug-sensitive BRCA1-deficient tumors showed DNA damage, but cells were largely viable. By applying paired statistics and quantitative filtering, we identified highly discriminatory markers for the sensitive and resistant model. Proteins up-regulated in the sensitive model are involved in centrosome organization, chromosome condensation, homology-directed DNA repair, and nucleotide metabolism. Major discriminatory markers that were up-regulated in the resistant model were predominantly involved in fatty acid metabolism, such as fatty-acid synthase. Specific inhibition of fatty-acid synthase sensitized resistant cells to cisplatin. Our data suggest that exploring the functional link between the DNA damage response and cancer metabolism shortly after the initial treatment may be a useful strategy to predict the efficacy of cisplatin.

In order to successfully enter the latent stage, Mycobacterium tuberculosismust adapt to conditions such as nutrient limitation and hypoxia. In vitromodels that mimic latent infection are valuable tools for describing the changes in metabolism that occur when the bacterium exists in a non-growing form. We used two complementary proteomic approaches, label-free LC-MS/MS analysis and two-dimensional difference gel electrophoresis, to determine the proteome profile of extracellular proteins from M. tuberculosiscultured under nutrient starvation. Through the label-free LC-MS/MS analysis of fractionated samples, 1176 proteins were identified from culture filtrates of log phase and nutrient-starved cultures, and the protein levels of 230 proteins were increased in nutrient-starved culture filtrates, whereas those of 208 proteins were decreased. By means of Gene Ontology clustering analysis, significant differences in the overall metabolism during nutrient starvation were detected. Notably, members of the toxin–antitoxin systems were present in larger quantities in nutrient-starved cultures, supporting a role for these global modules as M. tuberculosisswitches its metabolism into dormancy. Decreased abundance of proteins involved in amino acid and protein synthesis was apparent, as well as changes in the lipid metabolism. Further analysis of the dataset identified increased abundance of lipoproteins and decreased abundance of ESAT-6 family proteins. Results from the two-dimensional difference gel electrophoresis proteomics demonstrated overall agreement with the LC-MS/MS data and added complementary insights about protein degradation and modification.

This paper deals with the boundary control for scalar non-linearhyperbolic systems of conservation laws. One of the issues arising from these systems is the formation of singularities (called shocks) in the domain. We will show that to avoid the shock, a set of constraints for the control and the state at the boundary has to be fulfilled. Then, a proof of the exponential stability of the system is established provided that there is no shock and that the state at the boundary is exponentially stable. These conditions are shown to be achieved by the Receding Horizon Optimal Control approach. A simulation is finally carried out with the freeway traffic model to demonstrate the potential of here-proposed control.

Abnormalities in nuclear phenotype and chromosome structure are key features of cancer cells. Investigation of the protein determinants of nuclear subfractions in cancer may yield molecular insights into aberrant chromosome function and chromatin organization and in addition may yield biomarkers for early cancer detection. Here we evaluate a proteomics work flow for profiling protein constituents in subnuclear domains in colorectal cancer tissues and apply this work flow to a comparative analysis of the nuclear matrix fraction in colorectal adenoma and carcinoma tissue samples. First, we established the reproducibility of the entire work flow. In a reproducibility analysis of three nuclear matrix fractions independently isolated from the same colon tumor homogenate, 889 of 1,047 proteins (85%) were reproducibly identified at high confidence (minimally two peptides per protein at 99% confidence interval at the protein level) with an average coefficient of variance for the number of normalized spectral counts per protein of 30%. This indicates a good reproducibility of the entire work flow from biochemical isolation to nano-LC-MS/MS analysis. Second, using spectral counting combined with statistics, we identified proteins that are significantly enriched in the nuclear matrix fraction relative to two earlier fractions (the chromatin-binding and intermediate filament fractions) isolated from six colorectal tissue samples. The total data set contained 2,059 non-redundant proteins. Gene ontology mining and protein network analysis of nuclear matrix-enriched proteins revealed enrichment for proteins implicated in "RNA processing" and "mRNA metabolic process." Finally, an explorative comparison of the nuclear matrix proteome in colorectal adenoma and carcinoma tissues revealed many proteins previously implicated in oncogenesis as well as new candidates. A subset of these differentially expressed proteins also exhibited a corresponding change at the mRNA level. Together, the results show that subnuclear proteomics of tumor tissue is feasible and a promising avenue for exploring oncogenesis.

Malignant gliomas (glioblastoma multiforme) have a poor prognosis with an average patient survival under current treatment regimens ranging between 12 and 14 months. The tumors are characterized by rapid cell growth, extensive neovascularization, and diffuse cellular infiltration of normal brain structures. We have developed a human glioblastoma xenograft model in nude rats that is characterized by a highly infiltrative non-angiogenic phenotype. Upon serial transplantation this phenotype will develop into a highly angiogenic tumor. Thus, we have developed an animal model where we are able to establish two characteristic tumor phenotypes that define human glioblastoma (i.e. diffuse infiltration and high neovascularization). Here we aimed at identifying potential biomarkers expressed by the non-angiogenic and the angiogenic phenotypes and elucidating the molecular pathways involved in the switch from invasive to angiogenic growth. Focusing on membrane-associated proteins, we profiled protein expression during the progression from an invasive to an angiogenic phenotype by analyzing serially transplanted glioma xenografts in rats. Applying isobaric peptide tagging chemistry (iTRAQ) combined with two-dimensional LC and MALDI-TOF/TOF mass spectrometry, we were able to identify several thousand proteins in membrane-enriched fractions of which 1460 were extracted as quantifiable proteins (isoform- and species-specific and present in more than one sample). Known and novel candidate proteins were identified that characterize the switch from a non-angiogenic to a highly angiogenic phenotype. The robustness of the data was corroborated by extensive bioinformatics analysis and by validation of selected proteins on tissue microarrays from xenograft and clinical gliomas. The data point to enhanced intercellular cross-talk and metabolic activity adopted by tumor cells in the angiogenic compared with the non-angiogenic phenotype. In conclusion, we describe molecular profiles that reflect the change from an invasive to an angiogenic brain tumor phenotype. The identified proteins could be further exploited as biomarkers or therapeutic targets for malignant gliomas.

The total synthesis of raistrickindole A has been achieved, thereby confirming the proposed structure as an N-hydroxylated DKP. In the first but less selective approach, the DKP was built up by cyclization of a diastereoisomerically mixed N-hydroxylated dipeptide. In the second approach, the same DKP was constructed stereoselectively by the intramolecular Mitsunobu reaction of a hydroxamic acid. The synthesis was completed by a stereoselective oxidative cyclization.

Understanding structure at the interface between two-dimensional (2D) materials and 3D metals is crucial for designing novel 2D/3D heterostructures and improving the performance of many 2D material devices. Here, we quantify and discuss the 2D/3D interface structure and the 3D morphology in several materials systems. We first deposit faceted Au nanoislands on graphene and transition metal dichalcogenides, using measurements of the equilibrium island shape to determine values for the 2D/Au interface energy and examining the role of surface reconstructions, chemical identity, and defects on the grown structures. We then deposit the technologically relevant metals Ti and Nb under conditions where kinetic rather than thermodynamic factors govern growth. We describe a transition from dendritic to faceted islands as a function of growth temperature and discuss the factors determining island shape in these materials systems. Finally, we show that suspended 2D materials enable the fabrication of a novel type of 3D/2D/3D heterostructure and discuss the growth mechanism. We suggest that emerging nanodevices will utilize versatile fabrication of 2D/3D heterostructures with well-characterized interfaces and morphologies.

Single Co2+sites in graphitic carbon nitride (C3N4) have demonstrated excellent activity and selectivity in photocatalytic CO2reduction. In this work, we combine computational and spectroscopic tools, including X-ray absorption spectroscopy, to probe the structure of such single Co2+sites. Our results indicate that the Co2+sites exist in the Co–N2+2coordination at the edges of C3N4flakes. This mode of coordination is further supported by experimental results obtained using single Co2+sites in C3N4materials treated with NH3, which contain more edge sites than untreated C3N4. In our experimental observations, doping C3N4with carbon is found to be important for the photocatalytic properties of the single Co2+sites. A simplified model is proposed to explain the origin of the observed enhancement effect of C doping. In this model, the presence of C dopant near the metal centers results in shorter Co–N bond length and stronger Co–N binding energy. In addition to enhanced light absorption and charge separation in C-doped C3N4, the stronger Co–N binding upon C doping likely contributes to the improved catalytic properties of the single Co2+sites.

The rational design and synthesis of robust metal–organic frameworks (MOFs) based on novel organic building blocks are fundamental aspects of reticular chemistry. Beyond simply fabricating new organic linkers, however, it is important to elucidate structure–property relationships at the molecular level to develop high-performing materials. In this work, we successfully targeted a highly porous and robust cage-type MOF (NU-200) with an nbo-derived foftopology through the deliberate assembly of a cyclohexane-functionalized iron(II)-clathrochelate-based meta-benzenedicarboxylate linker with a Cu2(CO2)4secondary building unit (SBU). NU-200 exhibited an outstanding adsorption capacity of xenon and a high ideal adsorbed solution theory (IAST) predicted selectivity for a 20/80 v/v mixture of xenon (Xe)/krypton (Kr) at 298 K and 1.0 bar. Our extensive computational simulations with grand canonical Monte Carlo (GCMC) and density functional theory (DFT) on NU-200 indicated that the MOF’s hierarchical bowl-shaped nanopockets surrounded by custom-designed cyclohexyl groups─instead of the conventionally believed open metal sites (OMSs)─played a crucial role in reinforcing Xe-binding affinity. The optimally sized pockets firmly trapped Xe through numerous supramolecular interactions including Xe···H, Xe···O, and Xe···π. Additionally, we validated the unique pocket confinement effect by experimentally and computationally employing the similarly sized probe, sulfur dioxide (SO2), which provided significant insights into the molecular underpinnings of the high uptake of SO2(11.7 mmol g–1), especially at a low pressure of 0.1 bar (8.5 mmol g–1). This work therefore can facilitate the judicious design of organic building blocks, producing MOFs featuring tailor-made pockets to boost gas adsorption and separation performances.