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In this study, we conducted a theoretical analysis of thermal stress around an arbitrarily shaped hole in a thermoelectric material under electric current density and energy flux loading. Based on complex variable methods, conformal mapping, and analytical continuation theorem, the exact solutions of the thermal stress around a hole were obtained for the Seebeck coefficient and electric and heat conductivity. Based on the conversion efficiency equation of thermoelectric materials, higher electrical conductivity and lower heat conductivity should be selected to achieve an optimal design. The theoretical results indicated that higher electrical conductivity could reduce the thermal stress around the hole. However, energy flux and thermal stress concentration might be generated around the adiabatic hole due to the presence of a matrix with lower heat conductivity. Hence, thermoelectric materials with lower thermal conductivity should be selected carefully to avoid premature failure around the hole caused by thermal stress concentration. Finally, we also obtained and discussed the stress intensity factors of a hypocycloid-type crack.

Organic molecules having emission in the NIR(II) region are emergent and receiving enormous attention. Unfortunately, attaining accountable organic emission intensity around the NIR(II) region is hampered by the dominant internal conversion operated by the energy gap law, where the emission energy gap and the associated internal reorganization energy λ

Background: It aimed to the diagnosis and examination of acute myocardial infarction (AMI) using echocardiography under improved convolutional neural network (CNN) algorithm and high-sensitivity (Sen) cardiac troponin I (hs-cTnI) detection. The application effect was also evaluated. Methods: Ninety AMI patients were recruited as the AMI group, and ninety healthy individuals who underwent physical examinations simultaneously were chosen as control (Ctrl) group. Improved CNN algorithm-based echocardiography combined with hs-cTnI detection was applied, and its diagnostic efficiency was evaluated. Results: The optimal dataset scale (ODS), optimal image scale (OIS) and average precision (AP) of the proposed algorithm were better than those of manual labeling, Canny algorithm, and structured edge (SE) algorithm (P < 0.05). The left ventricular ejection fraction (LVEF) of the patients in the AMI group was inferior to that of Ctrl group ((55.09±2.78)%) versus (65.01±3.19)%), the left ventricular end-diastolic dimension (LVEDD) was superior to that of Ctrl group ((54.89±6.56) mm vs. (45.98±5.77) mm), and the cTnI level was also superior to that of Ctrl group ((2.90±0.31) pg/L vs. (0.73±0.42) pg/L) (P < 0.05). The diagnostic Sen (91.89%), specificity (Spe) (81.25%), accuracy (Acc) (90.00%) and consistency (0.56) of echocardiography combined with hs-cTnI were superior to those of single echocardiography or cTnI detection (P < 0.05).

Background: Cerebral small vessel disease (cSVD) is a major cause of stroke and dementia. Previous studies on the prevalence of cSVD are mostly based on single geographically defined cohorts in high-income countries. Studies investigating the prevalence of cSVD in low- and middle-income countries (LMICs) are expanding but have not been systematically assessed. Aim: This study aims to systematically review the prevalence of cSVD in LMICs. Results: Articles were searched from the Ovid MEDLINE and EMBASE databases from 1 January 2000 to 31 March 2022, without language restrictions. Title/abstract screening, full-text review, and data extraction were performed by two to seven independent reviewers. The prevalence of cSVD and study sample size were extracted by pre-defined world regions and health status. The Risk of Bias for Non-randomized Studies tool was used. The protocol was registered on PROSPERO (CRD42022311133). A meta-analysis of proportion was performed to assess the prevalence of different magnetic resonance imaging markers of cSVD, and a meta-regression was performed to investigate associations between cSVD prevalence and type of study, age, and male: female ratio. Of 2743 studies identified, 42 studies spanning 12 global regions were included in the systematic review. Most of the identified studies were from China (n = 23). The median prevalence of moderate-to-severe white matter hyperintensities (WMHs) was 20.5%, 40.5%, and 58.4% in the community, stroke, and dementia groups, respectively. The median prevalence of lacunes was 0.8% and 33.5% in the community and stroke groups. The median prevalence of cerebral microbleeds (CMBs) was 10.7% and 22.4% in the community and stroke groups. The median prevalence of moderate-to-severe perivascular spaces was 25.0% in the community. Meta-regression analyses showed that the weighted median age (51.4 ± 0.0 years old; range: 36.3–80.2) was a significant predictor of the prevalence of moderate-to-severe WMH and lacunes, while the type of study was a significant predictor of the prevalence of CMB. The heterogeneity of studies was high (>95%). Male participants were overrepresented. Conclusions: This systematic review and meta-analysis provide data on cSVD prevalence in LMICs and demonstrated the high prevalence of the condition. cSVD research in LMICs is being published at an increasing rate, especially between 2010 and 2022. More data are particularly needed from Sub-Saharan Africa and Central Europe, Eastern Europe, and Central Asia.

We perform a high-throughput screening on phonon-mediated superconductivity in a ternary metal diboride structure with alkali, alkaline earth, and transition metals. We find 17 ground states and 78 low-energy metastable phases. From fast calculations of zone-center electron-phonon coupling, 43 compounds are revealed to show electron-phonon coupling strength higher than that of MgB

This work aims to demonstrate a facile method for the controlled orientation of nanostructures of block copolymer (BCP) thin films. A simple diblock copolymer system, polystyrene

Laser-driven coherent phonons can act as modulated strain fields and modify the adiabatic ground state topology of quantum materials. We use time-dependent first-principles and effective model calculations to simulate the effect of a strong terahertz electric field on electronic carriers in the topological insulator ZrTe$_5$. We show that a coherent $A_\text{1g}$ Raman mode modulation can effectively pump carriers across the band gap, even though the phonon energy is about an order of magnitude smaller than the equilibrium band gap. We reveal the microscopic mechanism of this effect which occurs via Landau-Zener-St\"uckelberg tunneling of Bloch electrons in a narrow region in the Brillouin zone center where the transient energy gap closes when the system switches from strong to weak topological insulator. The quantum dynamics simulation results are in excellent agreement with recent pump-probe experiments in ZrTe$_5$ at low temperature., Comment: 11 pages, 4 figures

Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe 3 CoB 2 , through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe 3 CoB 2 exhibits a high magnetic anisotropy ( K 1 = 1.2 MJ/m 3 ) and saturation magnetic polarization ( J s = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.

Purpose: Multiplexing assay of biomarkers at the point-of-care is an elusive goal for molecular diagnostics.Experimental Design: Here, we report an electrochemical (EC) sensor for oral cancer detection based on the simultaneous detection of two salivary biomarkers: interleukin (IL)-8 mRNA and IL-8 protein.Results: Under the multiplexing mode, the limit of detection of salivary IL-8 mRNA reaches to 3.9 fM and 7.4 pg/mL for IL-8 protein in saliva. Multiplex assay of these 2 biomarkers directly from 28 cancer and 28 matched control saliva samples shows significant difference between the two groups. From the receiver operating characteristic analysis, the EC sensor yields around 90% sensitivity and specificity for both IL-8 mRNA and IL-8 protein, which are very close to the data measured by traditional assays (ELISA and PCR) with the same group of saliva. Combined IL-8 mRNA and protein show better AUC compared with single biomarker.Conclusions: We show, for the first time, concurrently multiplexing detection of salivary mRNA and protein biomarkers using point-of-care EC sensor.

Supplementary Data from Electrochemical Sensor for Multiplex Biomarkers Detection

PDF file, 2015K, Table S1. MMPs and PKCs primer pairs and PCR conditions for RT-PCR. Table S2. MMPs and PKCs primer pairs and PCR conditions for ChIP assay. Table S3. Antibodies for Western blot analysis. Figure S1. The remaining subset of human cancer cell lines without enhancement of cell migration activity after TSA treatment. Figure S2. Validation of HDACi-enhanced cell migration with treatments of 6 different HDACi. Figure S3. HDACi treatments of HCC and lung cancer cells enhance in vivo experimental metastasis. Figure S4. Treatment of HDACi modulated MMPs and PKCs expressions. Figure S5. The combination treatments of HDACi with MMPi or PKCi repress HDACi-enhanced migration activity. Figure S6. The effects of HDACi and PKCi on tumorigenesis, toxicity and tumor-progressive protein expressions in vivo. Figure S7. Suppressive effects of tumorigenesis and metastasis in liver metastasis model of TSA/Tam and TSA/Cur treated mice.

Bioinspired from structural coloration of butterfly wing structure, this work aims to fabricate nanoporous chitosan for UVC reflection. By taking advantage of self-assembled polystyrene-b-polydimethylsiloxane (PS-b-PDMS) with double gyroid texture followed by hydrofluoric acid etching of PDMS block, nanoporous PS with well-defined nanochannels can be fabricated, and used as a template for templated crosslinking reaction of chitosan through a multiple pore-filling process. Well-ordered nanoporous chitosan with shifting networks in nanoscale can be successfully fabricated after removal of the PS template. With the low absorption of chitosan in the ultraviolet region and the shifting networks for opening the bandgap, it is appealing to exploit the nanonetwork chitosan as high reflective materials for UVC optical devices, as evidenced by finite-difference time-domain (FDTD) simulation and optical measurements experimentally.

Purpose: Histone deacetylase inhibitors (HDACi) are actively explored as new-generation epigenetic drugs but have low efficacy in cancer monotherapy. To reveal new mechanism for combination therapy, we show that HDACi induce cell death but simultaneously activate tumor-progressive genes to ruin therapeutic efficacy. Combined treatments to target tumorigenesis and HDACi-activated metastasis with low toxic modalities could develop new strategies for long-term cancer therapy.Experimental Design: Because metastasis is the major cause of cancer mortality, we measured cell migration activity and profiled metastasis-related gene expressions in HDACi-treated cancer cells. We developed low toxic combination modalities targeting tumorigenesis and HDACi-activated metastasis for preclinical therapies in mice.Results: We showed that cell migration activity was dramatically and dose dependently enhanced by various classes of HDACi treatments in 13 of 30 examined human breast, gastric, liver, and lung cancer cell lines. Tumor metastasis was also enhanced in HDACi-treated mice. HDACi treatments activated multiple PKCs and downstream substrates along with upregulated proapoptotic p21. For targeting tumorigenesis and metastasis with immediate clinical impact, we showed that new modalities of HDACi combined drugs with PKC inhibitory agent, curcumin or tamoxifen, not only suppressed HDACi-activated tumor progressive proteins and cell migration in vitro but also inhibited tumor growth and metastasis in vivo.Conclusion: Treatments of different structural classes of HDACi simultaneously induced cell death and promoted cell migration and metastasis in multiple cancer cell types. Suppression of HDACi-induced PKCs leads to development of low toxic and long-term therapeutic strategies to potentially treat cancer as a chronic disease. Clin Cancer Res; 18(17); 4691–701. ©2012 AACR.

There has been a long debate on the stable phase of iron under the Earth's inner core conditions. Because of the solid-liquid coexistence at the inner core boundary, the thermodynamic stability of solid phases directly relates to their melting temperatures, which remains considerable uncertainty. In the present study, we utilized a semi-empirical potential fitted to high-temperature ab initio data to perform a thermodynamic integration from classical systems described by this potential to ab initio systems. This method provides a smooth path for thermodynamic integration and significantly reduces the uncertainty caused by the finite-size effect. Our results suggest the hcp phase is the stable phase of pure iron under the inner core conditions, while the free energy difference between the hcp and bcc phases is tiny, on the order of 10s meV/atom near the melting temperature., Comment: 4 figures

We study the time-dependence of Hawking radiation for a black hole in the Unruh vacuum, and find that it is not robust against certain UV and IR effects. If there is a UV cutoff at the Planck scale, Hawking radiation is turned off after the scrambling time. In the absence of a UV cutoff, Hawking radiation is sensitive to the IR cutoff through a UV/IR connection due to higher-derivative interactions in the effective theory. Furthermore, higher-derivative interactions with the background contribute to a large amplitude of particle creation that changes Hawking radiation. This unexpected large effect is related to a peculiar feature of the Hawking particle wave packets., 35 pages, 5 figures, references added, minor modification

Surface biotinylation has been widely adapted in profiling the cellular proteome associated with the plasma membrane. However, the workflow is subject to interference from the cytoplasmic biotin-associated proteins that compete for streptavidin-binding during purification. Here we established a bioorthogonal conjugation-assisted purification (BCAP) workflow that utilizes the Staudinger chemoselective ligation to label and isolate surface-associated proteins while minimizing the binding of endogenous biotin-associated proteins. Label-free quantitative proteomics demonstrated that BCAP is efficient in isolating cell surface proteins with excellent reproducibility. Subsequently, we applied BCAP to compare the surface proteome of proliferating and senescent mouse embryonic fibroblasts (MEFs). Among the results, EHD2 was identified and validated as a novel protein that is enhanced at the cell surface of senescent MEFs. We expect that BCAP will have broad applications in profiling cell surface proteomes in the future.

Cold-crystallized PDLA was studied by time-resolved VCD analysis, providing molecular level insights for crystallization via intra- and inter-chain chiral interactions.

In the background of a gravitational collapse, we compute the transition amplitudes for the creation of particles for distant observers due to higher-derivative interactions in addition to Hawking radiation. The amplitudes grow exponentially with time and become of order 1 when the collapsing matter is about a Planck length outside the horizon. As a result, the effective theory breaks down at the scrambling time, invalidating its prediction of Hawking radiation. Planckian physics comes into play to decide the fate of black-hole evaporation., Comment: 24 pages, no figure, reference added, minor modification

With the increasing incidence and mortality of human hepatocellular carcinoma (HCC) worldwide, revealing innovative targets to improve therapeutic strategies is crucial for prolonging the lives of patients. To identify innovative targets, we conducted a comprehensive comparative transcriptome analysis of 5,410 human HCCs and 974 mouse liver cancers to identify concordantly expressed genes associated with patient survival. Among the 664 identified prognostic comparative HCC (pcHCC) genes, upregulated pcHCC genes were associated with prognostic clinical features, including large tumor size, vascular invasion and late HCC stages. Interestingly, after validating HCC patient prognoses in multiple independent datasets, we matched the 664 aberrant pcHCC genes with the sorafenib-altered genes in TCGA_LIHC patients and found these 664 pcHCC genes were enriched in sorafenib-related functions, such as downregulated xenobiotic and lipid metabolism and upregulated cell proliferation. Therapeutic agents targeting aberrant pcHCC genes presented divergent molecular mechanisms, including suppression of sorafenib-unrelated oncogenic pathways, induction of sorafenib-unrelated ferroptosis, and modulation of sorafenib transportation and metabolism, to potentiate sorafenib therapeutic effects in HCC combination therapy. Moreover, the pcHCC genes NCAPG and CENPW, which have not been targeted in combination with sorafenib treatment, were knocked down and combined with sorafenib treatment, which reduced HCC cell viability based on disruption to the p38/STAT3 axis, thereby hypersensitizing HCC cells. Together, our results provide important resources and reveal that 664 pcHCC genes represent innovative targets suitable for developing therapeutic strategies in combination with sorafenib based on the divergent synergistic mechanisms for HCC tumor suppression.

Interatomic potentials based on neural-network machine learning method have attracted considerable attention in recent years owing to their outstanding ability to balance the accuracy and efficiency in atomistic simulations. In this work, a neural-network potential (NNP) for carbon is generated to simulate the structural properties of various carbon structures. The potential is trained using a database consisting of crystalline and liquid structures obtained by the first-principles density functional theory (DFT) calculations. The developed potential accurately predicts the energies and forces in crystalline and liquid carbon structures, the energetic stability of defected graphene, and the structures of amorphous carbon as the function of density. The excellent accuracy and transferability of the NNP provide a promising tool for accurate atomistic simulations of various carbon materials with faster speed and much lower cost.

A series of NIR-II fluorescent TADF-incorporated polymer dots were successfully synthesized. The function of the TADF moiety was fully studied and the bio-applications of these polymer dots including bone imaging were also demonstrated.

Soy intake is associated with lower breast cancer risk in observational studies concerning Asian women, however, no randomized controlled trials (RCT) have been conducted among Asian women living in Asia. This three-armed RCT assessed the effects of one-year soy isoflavone (ISF) intervention on mammographic density (MD) change among healthy peri- and postmenopausal Malaysian women. This study was registered at ClinicalTrials.gov (NCT03686098). Participants were randomized into the 100 mg/day ISF Supplement, 50 mg/day ISF Diet, or control arm, and assessed for change in absolute and relative dense area from digital mammograms conducted at enrolment and after 12 months, compared over time across study arms using Kruskal-Wallis tests. Out of 118 women enrolled, 91 women completed the intervention, while 27 women (23%) were lost in follow up. The ISF supplement arm participants observed a larger decline in dense area (−1.3 cm2), compared to the ISF diet (−0.5 cm2) and control arm (−0.8 cm2), though it was not statistically significant (p = 0.48). Notably, among women enrolled within 5 years of menopause; dense area declined by 6 cm2 in the ISF supplement arm, compared to

Type migration is the process of adding types to untyped code to gain assurance at compile time. TypeScript and other gradual type systems facilitate type migration by allowing programmers to start with imprecise types and gradually strengthen them. However, adding types is a manual effort and several migrations on large, industry codebases have been reported to have taken several years. In the research community, there has been significant interest in using machine learning to automate TypeScript type migration. Existing machine learning models report a high degree of accuracy in predicting individual TypeScript type annotations. However, in this paper we argue that accuracy can be misleading, and we should address a different question: can an automatic type migration tool produce code that passes the TypeScript type checker? We present TypeWeaver, a TypeScript type migration tool that can be used with an arbitrary type prediction model. We evaluate TypeWeaver with three models from the literature: DeepTyper, a recurrent neural network; LambdaNet, a graph neural network; and InCoder, a general-purpose, multi-language transformer that supports fill-in-the-middle tasks. Our tool automates several steps that are necessary for using a type prediction model, including (1) importing types for a project’s dependencies; (2) migrating JavaScript modules to TypeScript notation; (3) inserting predicted type annotations into the program to produce TypeScript when needed; and (4) rejecting non-type predictions when needed. We evaluate TypeWeaver on a dataset of 513 JavaScript packages, including packages that have never been typed before. With the best type prediction model, we find that only 21% of packages type check, but more encouragingly, 69% of files type check successfully., LIPIcs, Vol. 263, 37th European Conference on Object-Oriented Programming (ECOOP 2023), pages 37:1-37:28

Type migration is the process of adding types to untyped code to gain assurance at compile time. TypeScript and other gradual type systems facilitate type migration by allowing programmers to start with imprecise types and gradually strengthen them. However, adding types is a manual effort and several migrations on large, industry codebases have been reported to have taken several years. In the research community, there has been significant interest in using machine learning to automate TypeScript type migration. Existing machine learning models report a high degree of accuracy in predicting individual TypeScript type annotations. However, in this paper we argue that accuracy can be misleading, and we should address a different question: can an automatic type migration tool produce code that passes the TypeScript type checker? We present TypeWeaver, a TypeScript type migration tool that can be used with an arbitrary type prediction model. We evaluate TypeWeaver with three models from the literature: DeepTyper, a recurrent neural network; LambdaNet, a graph neural network; and InCoder, a general-purpose, multi-language transformer that supports fill-in-the-middle tasks. Our tool automates several steps that are necessary for using a type prediction model, including (1) importing types for a project’s dependencies; (2) migrating JavaScript modules to TypeScript notation; (3) inserting predicted type annotations into the program to produce TypeScript when needed; and (4) rejecting non-type predictions when needed. We evaluate TypeWeaver on a dataset of 513 JavaScript packages, including packages that have never been typed before. With the best type prediction model, we find that only 21% of packages type check, but more encouragingly, 69% of files type check successfully., DARTS, Vol. 9, Special Issue of the 37th European Conference on Object-Oriented Programming (ECOOP 2023), pages 5:1-5:3