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A cyclic base ordering (CBO) of a connected graph [Formula: see text] is a cyclic ordering of [Formula: see text] such that every cyclically consecutive [Formula: see text] edge induces a spanning tree of [Formula: see text]. The density of [Formula: see text] is defined to be [Formula: see text]; and [Formula: see text] is uniformly dense if [Formula: see text] for every connected subgraph [Formula: see text] of [Formula: see text]. It was conjectured by Kajitani, Ueno and Miyano that [Formula: see text] has a CBO if and only if [Formula: see text] is uniformly dense. In this paper, we study CBO of generalized Petersen graphs to support this conjecture, and we prove that [Formula: see text] and [Formula: see text] have CBO.

Freeze-drying is an effective approach to improve the long-term stability of nanomedicines. Lyoprotectants are generally considered as requisite excipients to ensure that the quality of nanoparticles is maintained throughout the freeze-drying process. However, depending on the type of nanoparticles, the needs for lyoprotectants or the challenges they face during freeze-drying may be different. In this study, we compared and identified the impact of freeze-drying on key characteristics of three types of nanoparticles: solid lipid nanoparticles (SLNs), polymeric nanoparticles (PNs), and liposomes. Sucrose, trehalose, and mannitol were added to nanoparticle suspensions before freeze-drying. The same conservative freeze-drying conditions with controlled ice nucleation at −8 °C were employed for all formulations. The collapse temperatures of nanoparticle formulations were found to be the same as those of the lyoprotectant added, except PN formulation. Likely the poly(vinyl alcohol) (PVA) in the formulation induced a higher collapse temperature and retardation of drying of PNs. Freeze-drying of both SLNs and liposomes without lyoprotectants increased particle size and polydispersity, which was resolved by adding amorphous disaccharides. Regardless of the addition of lyoprotectants, freeze-drying did not alter the size of PNs possibly due to the protection from PVA. However, lyoprotectants were still necessary to shorten the reconstitution time and reduce the residual moisture. In conclusion, different types of nanoparticles face distinct challenges for freeze-drying, and lyoprotectants differentially affect various stability and quality attributes of freeze-dried nanoparticles.

Instagram has become one of the most widely used social media platforms globally. As such, the volume of information generated by Instagram users makes it an excellent data mine to study human behaviour. Although researchers have turned to Twitter data to observe the flow of misinformation surrounding the COVID-19 pandemic, there is little to no publication on COVID-19 behaviours on Instagram. To address this knowledge gap, an extensive analysis was conducted on COVID-19-related big data from Instagram. 5,300 global Instagram posts were collected between January 5 and March 20 of 2020, and the associated hashtags were processed using a novel, highly extensible Social Network Analysis pipeline. Eigenvector centrality and weight cluster indexing partitioned the hashtag data into eleven clusters, three of which were studied to reflect public behaviour. The data revealed that the mental health cluster consisted mainly of Instagram business profiles and that these were associated with positive sentiments on COVID-19. It was found that a niche cluster containing conspiracy-related content was primarily created by real Instagram users and was associated with very negative sentiments. A supervised machine learning approach was also used to classify hashtags related to comedy and memes as bot-generated content. For future investigations, it is recommended that a large-scale behavioural analysis of bots and their effects on pandemic information be performed.

Author(s): Saber, Muna; Preefer, Molleigh B; Kolli, Sanjeev K; Zhang, William; Laurita, Geneva; Dunn, Bruce; Seshadri, Ram; Van der Ven, Anton

Database management systems (DBMSs) are notoriously difficult to deploy and administer. Self-driving DBMSs seek to remove these impediments by managing themselves automatically. Despite decades of DBMS auto-tuning research, a truly autonomous, self-driving DBMS is yet to come. But recent advancements in artificial intelligence and machine learning (ML) have moved this goal closer. Given this, we present a system implementation treatise towards achieving a self-driving DBMS. We first provide an overview of the NoisePage self-driving DBMS that uses ML to predict the DBMS's behavior and optimize itself without human support or guidance. The system's architecture has three main ML-based components: (1) workload forecasting, (2) behavior modeling, and (3) action planning. We then describe the system design principles to facilitate holistic autonomous operations. Such prescripts reduce the complexity of the problem, thereby enabling a DBMS to converge to a better and more stable configuration more quickly.

The properties of crystalline materials tend to be strongly correlated with their structures, and the prediction of crystal structure from only the composition is a coveted goal in the field of inorganic materials. However, even for the simplest compositions, such prediction relies on a complex network of interactions, including atomic or ionic radii, ionicity, electronegativity, position in the periodic table, and magnetism, to name only a few important parameters. We focus here on the AB2X6 (AB2O6 and AB2F6) composition space with the specific goal of finding new oxide compounds in the trirutile family, which is known for unusual one-dimensional (1D) antiferromagnetic behavior. Through machine learning methods, we develop an understanding of how geometric and bonding constraints determine the crystallization of compounds in the trirutile structure as opposed to other ternary structures in this space. In combination with density functional theory (DFT) calculations, we predict 16 previously unreported candidate trirutile oxides. We successfully prepare one of these and show it forms in the disordered rutile structure, under the preparation conditions adopted here.

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic. As the 2019 coronavirus disease continues to spread, it will be useful to have as many effective treatment options as possible. This research has the potential to create a siRNA treatment that is safe, effective, and practical in design and administration; 192 siRNAs were designed to target conserved regions of the SARS-CoV-2 genome. The first aim of this study is to confirm, via sequence analysis, that these target sites have remained highly conserved over the course of the pandemic. Multiple sequence alignments were generated for the first half of 30,312 full SARS-CoV-2 genomes, which were averaged and compared with the Wuhan-Hu-1 reference genome. Most target sites maintained a very high level of conservation, suggesting that potential repressor siRNAs could be effective in a majority of infected individuals. To evaluate the efficacy of the 192 test siRNAs, we cloned sections of the SARS-CoV-2 RNA genome into GFP fusion genes. Some of these constructs were transfected in different conditions to set up a screening assay based on GFP expression. Preliminary data on the setup of this GFP reporter assay show that the M, N, E, ORF8, and ORF10 constructs produced a good GFP signal, whereas the S, F1, F2 and F3 constructs did not produce a sufficiently strong GFP signal to detect above background. In a preliminary experiment, we evaluated siRNAs targeting the M, N, and E open reading frames and found some to be efficacious. Future directions for this project include generating alignments of the second half of the SARS-CoV-2 genome for a complete sequence conservation estimate, and cell metabolism assays for supplementing visual observations of siRNA toxicity, optimization of GFP readout, and screening of all designed siRNAs.

PNb9O25, a Wadsley–Roth compound whose structure is obtained by appropriate crystallographic shear of the ReO3 structure, is a high-power electrode material that can reach 85% of the equilibrium ca...

Protein homeostasis, proteostasis, is essential for healthy cell functioning and is dysregulated in many diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled online to mass spectrometry (LC-MS) is a powerful high-throughput technique to study proteome dynamics in vivo. Longer labeling duration and dense timepoint sampling (TPS) of tissues provide accurate proteome dynamics estimations. However, the experiments are expensive, and they require animal housing and care, as well as the labeling with stable isotopes. Often the animals are sacrificed at selected timepoints to collect tissues. Therefore, it is necessary to optimize TPS for a given number of sampling points and labeling duration and target a specific tissue of study. Currently, such techniques are missing in proteomics. Here, we report on a formula-based stochastic simulation strategy for TPS for in vivo studies with heavy water metabolic labeling and LC-MS. We model the rate constant (lognormal), measurement error (Laplace), peptide length (Gamma), relative abundance (RA) of the monoisotopic peak (beta regression), and the number of exchangeable hydrogens (Gamma regression). The parameters of the distributions are determined using corresponding empirical probability density functions from a large-scale dataset of murine heart proteome. The models are used in simulations of the rate constant to minimize the root-mean-squared error (RMSE). The RMSE for different TPSs shows structured patterns. They are analyzed to elucidate common features in the patterns.

Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies.

The macrophage checkpoint interaction CD47-SIRPα is an emerging target for cancer therapy, but clinical trials of monoclonal anti-CD47 show efficacy only in liquid tumors when combined with tumor-opsonizing IgG. Here, in challenging metastatic solid tumors, CD47 deletion shows no effect on tumor growth unless combined with otherwise ineffective tumor-opsonization, and we likewise show wild-type metastases are suppressed by SIRPα-blocked macrophages plus tumor-opsonization. Lung tumor nodules of syngeneic B16F10 melanoma cells with CD47 deletion show opsonization drives macrophage phagocytosis of B16F10s, consistent with growth versus phagocytosis calculus for exponential suppression of cancer. Wild-type CD47 levels on metastases in lungs of immunocompetent mice and on human metastases in livers of immunodeficient mice show that systemic injection of antibody-engineered macrophages also suppresses growth. Such in vivo functionality can be modulated by particle pre-loading of the macrophages. Thus, even though CD47-SIRPα disruption and tumor-opsonizing IgG are separately ineffective against established metastatic solid tumors, their combination in molecular and cellular therapies prolongs survival.

Macrophages are abundant in solid tumours and typically associate with poor prognosis, but macrophage clusters in tumour nests have also been reported as beneficial even though dispersed macrophages would have more contacts with cancer cells. Here, by maximizing both phagocytic activity and macrophage numbers, we discover cooperative phagocytosis by low entropy clusters in rapidly growing engineered immuno-tumouroids. The results fit the calculus of proliferation-versus-engulfment, and rheological measurements and molecular perturbations provide a basis for understanding phagocytic disruption of a tumour’s cohesive forces in soft cellular phases. The perturbations underscore the utility of suppressing a macrophage checkpoint in combination with an otherwise ineffective tumour-opsonizing monoclonal antibody, and the approach translates in vivo to tumour elimination that durably protects mice from re-challenge and metastasis. Adoptive transfer of engineered macrophages increases the fraction of mice that eliminate tumours and potentially overcomes checkpoint blockade challenges in solid tumours like insufficient permeation of blocking antibodies and on-target, off-tumour binding. Finally, anti-cancer IgG induced in vivo are tumour-specific but multi-epitope and contribute to a phagocytic feedback that drives macrophage clustering in vitro. Given that solid tumours remain challenging for immunotherapies, durable anti-tumour responses here illustrate unexpected advantages in maximizing net phagocytic activity.

We demonstrated a low-cost method to measure the undissolved impurities in water and proposed the design for a portable device. This device can be quickly transferred onto assembly lines, significantly changing people’s lives in rural regions.

We proposed a new pump-probe microscope with improved imaging throughput and imaging sensitivity compared to existing pump-probe microscopes. It will significantly broaden the spectrum of molecular imaging in fundamental cell biology, disease diagnosis, and nanomaterials.

Deep learning perception models require a massive amount of labeled training data to achieve good performance. While unlabeled data is easy to acquire, the cost of labeling is prohibitive and could create a tremendous burden on companies or individuals. Recently, self-supervision has emerged as an alternative to leveraging unlabeled data. In this paper, we propose a new light-weight self-supervised learning framework that could boost supervised learning performance with minimum additional computation cost. Here, we introduce a simple and flexible multi-task co-training framework that integrates a self-supervised task into any supervised task. Our approach exploits pretext tasks to incur minimum compute and parameter overheads and minimal disruption to existing training pipelines. We demonstrate the effectiveness of our framework by using two self-supervised tasks, object detection and panoptic segmentation, on different perception models. Our results show that both self-supervised tasks can improve the accuracy of the supervised task and, at the same time, demonstrates strong domain adaption capability when used with additional unlabeled data., 2021 IEEE Intelligent Vehicles Symposium

Abstract: Local anesthetics are voltage-gated sodium channel blockers primarily administered locally or to the innervating nerves for anesthetic or analgesic purposes. In vitro studies have found direct effects of local anesthetics on cancer cells, such as impact on cancer cell proliferation, apoptosis, migration, invasion, and chemosensitivity, by multiple mechanisms. So far, in vivo evidence regarding the effect of local anesthetics on cancer cell lines is relatively lacking. Local and regional anesthesia administration has been reported to reduce postoperative pain and opioid use in cancer treatment. Additionally, regional anesthesia may reduce the perioperative stress response. However, the clinical therapeutic application of local anesthetics in cancer remains exploratory. In this review, we will discuss the direct and indirect effects of local anesthetics on cancer cells, and discuss the current evidence related to the use of local anesthetics in the treatment of cancer.

The properties of crystalline materials tend to be strongly correlated with their structures, and the prediction of crystal structure from only the composition is a coveted goal in the field of inorganic materials. However, even for the simplest compositions, such prediction relies on a complex network of interactions, including atomic or ionic radii, ionicity, electronegativity, position in the periodic table, and magnetism, to name only a few important parameters. We focus here on the AB

Glass and glass-ceramic samples of metastable lithium thiophosphates with compositions of 70Li2S-30P2S5 and Li7P3S11 were controllably prepared by using a rapid assisted-microwave procedure in under 30 min. The rapid preparation times and weak coupling of the evacuated silica ampules with microwave radiation ensure minimal reactivity of the reactants and the container. The microwave-prepared samples display comparable conductivity values with more conventionally prepared (melt quenched) glass and glass-ceramic samples, on the order of 0.1 and 1 mS cm-1 at room temperature, respectively. Rietveld analysis of synchrotron X-ray diffraction data acquired with an internal standard quantitatively yields phase amounts of the glassy and amorphous components, establishing the tunable nature of the microwave preparation. X-ray photoelectron spectroscopy and Raman spectroscopy confirm the composition and the appropriate ratios of isolated and corner-sharing tetrahedra in these semicrystalline systems. Solid-state 7Li nuclear magnetic resonance (NMR) spectroscopy resolves the seven crystallographic Li sites in the crystalline compound into three main environments. The diffusion behavior of these Li environments as obtained from pulsed-field gradient NMR methods can be separated into one slow and one fast component. The rapid and tunable approach to the preparation of high quality "Li7P3S11" samples presented here coupled with detailed structural and compositional analysis opens the door to new and promising metastable solid electrolytes.

A major hallmark of cancer is a perturbed metabolism resulting in high demand for various metabolites, glucose being the most well studied. While glucose can be converted into pyruvate for ATP production, the serine synthesis pathway (SSP) can divert glucose to generate serine, glycine, and methionine. In the process, the carbon unit from serine is incorporated into the one-carbon pool which makes methionine and maintains S-adenosylmethionine levels, which are needed to maintain the epigenetic landscape and ultimately controlling what genes are available for transcription. Alternatively, the carbon unit can be used for purine and thymidylate synthesis. We present here an approach to follow the flux through this pathway in cultured human cells using stable isotope enriched glucose and gas chromatography mass spectrometry analysis of serine, glycine, and methionine. We demonstrate that in three different cell lines this pathway contributes only 1-2% of total intracellular methionine. This suggests under high extracellular methionine conditions, the predominance of carbon units from this pathway are used to synthesize nucleic acids.

Mit der Entdeckung der intrinsisch fotosensitiven retinalen Ganglienzellen konnte 2001 gezeigt werden, dass die fotometrische Gr����e Beleuchtungsst��rke ungeeignet ist, um nicht-visuelle Lichteffekte auf die n��chtliche Melatoninunterdr��ckung, den zirkadianen Rhythmus, die subjektive Wachheit etc. zu beschreiben. Im Zuge dessen wurde u. a. die Gr����e Circadian Stimulus entwickelt, die die zirkadiane Lichtwirkung im Vergleich zur Beleuchtungsst��rke besser beschreibt. Die Anwendung dieser Gr����e in der Beleuchtungspraxis war jedoch bislang schwierig, da in der Lichtplanung ��berwiegend foto- und farbmetrische Gr����en verwendet werden. Die Bestimmung des Circadian Stimulus erfordert allerdings zur Messung des Lichtspektrums ein Spektrometer, das in der Lichtplanung meist keine Verwendung findet. Dieser Umstand wirft die Frage auf, inwieweit der Circadian Stimulus aus bekannten foto- und farbmetrischen Gr����en ermittelt werden kann. Deshalb befasst sich der erste Teil dieser Arbeit mit der Entwicklung von Berechnungsmodellen f��r den Circadian Stimulus, die nur auf den bekannten foto- und farbmetrischen Gr����en basieren. Hieraus entstanden zwei Berechnungsmodelle, die den Circadianen Stimulus mit der Beleuchtungsst��rke Ev und dem Farbort z2�� CIE-1931-Farbraum) bzw. der ��hnlichsten Farbtemperatur CCT bestimmen. Die meisten Forschungsergebnisse zu nicht-visuellen Lichtwirkungen stammen aus Laborstudien, die unter Randbedingungen durchgef��hrt wurden, welche im Alltag nicht vorkommen. Es stellt sich daher die Frage, inwieweit diese Erkenntnisse auf die angewandte Lichttechnik (z. B. Lichtplanung) ��bertragbar sind. In diesem Zusammenhang kommen Feldstudien ins Spiel, deren Randbedingungen der Realit��t sehr nahe kommen. Allerdings sind Feldstudien, die nicht-visuelle Beleuchtungseffekte untersuchen, relativ selten. Eine Ausnahme bilden hier Feldstudien mit rotierenden Dreischichtsystemen, die aus medizinischer Sicht zu bewerten sind und daher in dieser Arbeit keine Ber��cksichtigung finden. Der zweite Teil der vorliegenden Arbeit besch��ftigt sich mit Langzeit-Feldstudien zum Einfluss von Licht auf die Schlafqualit��t und Schl��frigkeit von Fr��hschichtarbeitenden im industriellen Umfeld. Die Studienergebnisse zeigen, dass Licht die Schl��frigkeit von sp��ten Chronotypen w��hrend der Fr��hschicht akut reduziert. Im Gegensatz dazu wurde kein direkter Zusammenhang zwischen der Lichtexposition w��hrend der Arbeitszeit und der Schlafqualit��t gefunden. Die Arbeit belegt die nicht-visuelle Lichtwirkung im industriellen Umfeld und zeigt in der praxisnahen Studie eine m��gliche Umsetzung einer Beleuchtungsl��sung, die die nicht-visuelle Wirkung gezielt in der Lichtplanung ber��cksichtigt.

The solid solution $\mathrm{Ba}{\mathrm{Co}}_{1\ensuremath{-}x}{\mathrm{Ni}}_{x}{\mathrm{S}}_{2}$ exhibits an insulator-to-metal transition close to $x=0.21$. Questions of whether this transition is coupled with structural changes remain open. Here we follow the structural evolution as a function of the Ni content $x$ using synchrotron powder x-ray diffraction and pair distribution function analyses to reveal significant basal sulfide anion displacements occurring preferentially along the ${\mathrm{CoS}}_{5}$ pyramidal edges comprising the edge-connected bond network in $\mathrm{Ba}{\mathrm{Co}}_{1\ensuremath{-}x}{\mathrm{Ni}}_{x}{\mathrm{S}}_{2}$. These displacements decrease in magnitude as $x$ increases and are nearly quenched in $x=1\phantom{\rule{4pt}{0ex}}{\mathrm{BaNiS}}_{2}$. Density-functional-theory-based electronic structure calculations on $x=0\phantom{\rule{4pt}{0ex}}{\mathrm{BaCoS}}_{2}$ suggest that these displacements arise as a dynamic first-order Jahn-Teller effect owing to partial occupancy of nominally degenerate ${\mathrm{Co}}^{2+}\phantom{\rule{4pt}{0ex}}{d}_{xz}$ and ${d}_{yz}$ orbitals, leading to local structural symmetry breaking in the $xy$-plane of the Co-rich phases. The Jahn-Teller instability is associated with the opening of a band gap that is further strengthened by electronic correlation. The Jahn-Teller effect is reduced upon increased electron filling as $x\ensuremath{\rightarrow}1$, indicating that the local structure and band filling cooperatively result in the observed insulator-to-metal transition.

第20回宇宙科学シンポジウム (2020年1月8日-9日. 宇宙航空研究開発機構宇宙科学研究所(JAXA)(ISAS)相模原キャンパス), 相模原市, 神奈川県, 20th Space Science Symposium (January 8-9, 2020. Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency(JAXA)(ISAS)Sagamihara Campus), Sagamihara, Kanagawa Japan, 著者人数: 19名, 資料番号: SA6000152021, レポート番号: S5-005

Purpose To compare cellular uptake and cytotoxicity of fluorescein (FL)-labeled polyethylene glycols (PEGs) carrying 2 folate groups (targeted delivery vehicles [TDVs]) to non-PEGylated molecules with 1 or 2 folate groups. Materials and Methods Three PEGylated TDVs and 2 non-PEGylated folic acid (FA)–fluorescein (FL) conjugates (FA-FL and FA-FL-FA) were synthesized. Two triple-negative breast cancer cell lines (MDA-MB-231and MDA-MB-468) were cultured to 70% confluency and incubated for 2 h in a folate-depleted medium. Folate receptor (FR) expression was confirmed by immunocytochemistry. Cellular uptake and cytotoxicity of compounds were measured by flow cytometry. Intracellular localization was confirmed using confocal microscopy. Results MDA-MB-231 demonstrated 40% more FR staining than MD-MB-468. Intracellular localization of the 2 non-PEGylated molecules (FA-FL and FA-FL-FA) and the 3 PEGylated TDVs was confirmed with confocal microscopy. Cellular uptake was independent of concentration for FA-FL, but there was 26.8% more cytotoxicity at 30 μg/mL compared with no treatment (P ≤ .05). Uptake was > 90% for FA-FL-FA at 10 μg/mL and 30 μg/mL without significant cytotoxicity (P ≤ .005). Cellular uptake was > 80% for all TDVs. The molecule containing monodispersed PEG with Mn = 1,000 g/mol had the highest uptake in both cell lines without cytotoxicity. Maximum toxicity was demonstrated by the molecule containing PEG2,000 only at the highest dose of 30 μg/mL (8.66% ± 3.94% cytotoxicity; cut-off was 20%). Conclusions The molecule containing monodispersed PEG with Mn = 1,000 g/mol and 2 FA targeting groups demonstrated better targetability and cellular uptake as a TDV.