Study on Yield Stress and Thixotropy of Hydroxypropyl Distarch Phosphate Paste

In order to study the yield stress and thixotropic behavior of the hydroxypropyl distarch phosphate (HPDSP) paste, HPDSP respectively derived from corn starch (CS) and waxy corn starch (WS) with different ratios of amylopectin were investigated. The critical mass fractions, yield stress, and thixotropic behavior of HPDSP pastes under various temperatures were studied. The results showed that, the critical mass fractions for the transition of the HPDSP solution at 5 ℃ from dilute to semi-dilute, and from semi-dilute to concentrated were 3wt% and 6wt%, respectively. The yield stress of 5wt% corn starch-hydroxypropyl distarch phosphate (CS-HPDSP) and waxy corn starch-hydroxypropyl distarch phosphate (WS-HPDSP) paste both showed weak correlations with temperature. However, at 6wt% concentration, the yield stress significantly decreased (P<0.05) by 69.52% and 77.95% respectively at 85 ℃. Additionally, the thixotropic behavior of HPDSP was influenced by both mass fraction and temperature. At 5 ℃, 5wt% CS-HPDSP and WS-HPDSP showed limited thixotropy, while at 6wt% of mass fraction, the areas of thixotropic loops of CS-HPDSP and WS-HPDSP were 163.49 and 85.00 Pa/s, respectively, and decreased by 86.38% and 92.18% at 85 ℃, respectively. WS-HPDSP exhibited less thixotropic behavior than CS-HPDSP, and showed better stability in three interval thixotropy test (3iTT). In conclusion, WS-HPDSP showed less yield stress and thixotropy compared with CS-HPDSP. This study provides theoretical supports for practical application of HPDSP as thickening agents in food products

Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective

Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the relationship among different distance metrics. In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification. We start from a naive baseline of confidence summation and demonstrate the necessity of exploiting the complementary property of different distance metrics. By finding the competition problem among them, built upon the baseline, we propose an Adaptive Metrics Module (AMM) to decouple metrics fusion into metric-prediction fusion and metric-losses fusion. The former encourages mutual complementary, while the latter alleviates metric competition via multi-task collaborative learning. Based on AMM, we design a few-shot classification framework AMTNet, including the AMM and the Global Adaptive Loss (GAL), to jointly optimize the few-shot task and auxiliary self-supervised task, making the embedding features more robust. In the experiment, the proposed AMM achieves 2% higher performance than the naive metrics fusion module, and our AMTNet outperforms the state-of-the-arts on multiple benchmark datasets

Prognostic and recurrent significance of SII in patients with pancreatic head cancer undergoing pancreaticoduodenectomy

BackgroundTo investigate the clinical significance of preoperative inflammatory status in patients with pancreatic head carcinoma (PHC), we performed a single-center study to assess it.MethodWe studied a total of 164 patients with PHC undergoing PD surgery (with or without allogeneic venous replacement) from January 2018 to April 2022. Systemic immune-inflammation index (SII) was the most important peripheral immune index in predicting the prognosis according to XGBoost analysis. The optimal cutoff value of SII for OS was calculated according to Youden index based on the receiver operating characteristic (ROC) curve and the cohort was divided into Low SII group and High SII group. Demographic, clinical data, laboratory data, follow-up data variables were obtained and compared between the two groups. Kaplan-Meier curves, univariable and multivariable Cox regression models were used to determine the association between preoperative inflammation index, nutritional index and TNM staging system with OS and DFS respectively.ResultsThe median follow-up time was 16 months (IQR 23), and 41.4% of recurrences occurred within 1 year. The cutoff value of SII was 563, with a sensitivity of 70.3%, and a specificity of 60.7%. Peripheral immune status was different between the two groups. Patients in High SII group had higher PAR, NLR than those in Low SII group (P &lt;0.01, &lt;0.01, respectively), and lower PNI (P &lt;0.01). Kaplan–Meier analysis showed significantly poorer OS and DFS (P &lt; 0.001, &lt;0.001, respectively) in patients with high SII. By using the multivariable Cox regression model, high SII (HR, 2.056; 95% CI, 1.082–3.905, P=0.028) was significant predictor of OS. Of these 68 high-risk patients who recurrence within one year, patients with widespread metastasis had lower SII and worse prognosis (P &lt;0.01).ConclusionHigh SII was significantly associated with poor prognosis in patients with PHC. However, in patients who recurrence within one year, SII was lower in patients at TNM stage III. Thus, care needs to be taken to differentiate those high-risk patients

MMDB: annotating protein sequences with Entrez's 3D-structure database

Three-dimensional (3D) structure is now known for a large fraction of all protein families. Thus, it has become rather likely that one will find a homolog with known 3D structure when searching a sequence database with an arbitrary query sequence. Depending on the extent of similarity, such neighbor relationships may allow one to infer biological function and to identify functional sites such as binding motifs or catalytic centers. Entrez's 3D-structure database, the Molecular Modeling Database (MMDB), provides easy access to the richness of 3D structure data and its large potential for functional annotation. Entrez's search engine offers several tools to assist biologist users: (i) links between databases, such as between protein sequences and structures, (ii) pre-computed sequence and structure neighbors, (iii) visualization of structure and sequence/structure alignment. Here, we describe an annotation service that combines some of these tools automatically, Entrez's ‘Related Structure’ links. For all proteins in Entrez, similar sequences with known 3D structure are detected by BLAST and alignments are recorded. The ‘Related Structure’ service summarizes this information and presents 3D views mapping sequence residues onto all 3D structures available in MMDB ()

Novel hypoxia-related gene signature for predicting prognoses that correlate with the tumor immune microenvironment in NSCLC

Background: Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. The global influence of hypoxia-related genes (HRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response is unclear in patients with non-small cell lung cancer (NSCLC).Method: RNA-seq and clinical data for NSCLC patients were derived from The Cancer Genome Atlas (TCGA) database, and a group of HRGs was obtained from the MSigDB. The differentially expressed HRGs were determined using the limma package; prognostic HRGs were identified via univariate Cox regression. Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, an optimized prognostic model consisting of nine HRGs was constructed. The prognostic model’s capacity was evaluated by Kaplan‒Meier survival curve analysis and receiver operating characteristic (ROC) curve analysis in the TCGA (training set) and GEO (validation set) cohorts. Moreover, a potential biological pathway and immune infiltration differences were explained.Results: A prognostic model containing nine HRGs (STC2, ALDOA, MIF, LDHA, EXT1, PGM2, ENO3, INHA, and RORA) was developed. NSCLC patients were separated into two risk categories according to the risk score generated by the hypoxia model. The model-based risk score had better predictive power than the clinicopathological method. Patients in the high-risk category had poor recurrence-free survival in the TCGA (HR: 1.426; 95% CI: 0.997–2.042; p = 0.046) and GEO (HR: 2.4; 95% CI: 1.7–3.2; p &lt; 0.0001) cohorts. The overall survival of the high-risk category was also inferior to that of the low-risk category in the TCGA (HR: 1.8; 95% CI: 1.5–2.2; p &lt; 0.0001) and GEO (HR: 1.8; 95% CI: 1.4–2.3; p &lt; 0.0001) cohorts. Additionally, we discovered a notable distinction in the enrichment of immune-related pathways, immune cell abundance, and immune checkpoint gene expression between the two subcategories.Conclusion: The proposed 9-HRG signature is a promising indicator for predicting NSCLC patient prognosis and may be potentially applicable in checkpoint therapy efficiency prediction

Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications

Monopolistic pricing models for revenue management are widely used in practice to set prices of multiple products with uncertain demand arrivals. The literature often assumes deterministic time of serving each demand and that the distribution of uncertainty is fully known. In this paper, we consider a new class of revenue management problems inspired by emerging applications such as cloud computing and city parking, where we dynamically determine prices for multiple products sharing limited resource and aim to maximize the expected revenue over a finite horizon. Random demand of each product arrives in each period, modeled by a function of the arrival time, product type, and price. Unlike the traditional monopolistic pricing, here each demand stays in the system for uncertain time. Both demand and service time follow ambiguous distributions, and we formulate robust deterministic approximation models to construct efficient heuristic fixed-price pricing policies. We conduct numerical studies by testing cloud computing service pricing instances based on data published by the Amazon Web Services (AWS) and demonstrate the efficacy of our approach for managing revenue and risk under various distributions of demand and service time

PubChem3D: a new resource for scientists

<p>Abstract</p> <p>Background</p> <p>PubChem is an open repository for small molecules and their experimental biological activity. PubChem integrates and provides search, retrieval, visualization, analysis, and programmatic access tools in an effort to maximize the utility of contributed information. There are many diverse chemical structures with similar biological efficacies against targets available in PubChem that are difficult to interrelate using traditional 2-D similarity methods. A new layer called PubChem3D is added to PubChem to assist in this analysis.</p> <p>Description</p> <p>PubChem generates a 3-D conformer model description for 92.3% of all records in the PubChem Compound database (when considering the parent compound of salts). Each of these conformer models is sampled to remove redundancy, guaranteeing a minimum (non-hydrogen atom pair-wise) RMSD between conformers. A diverse conformer ordering gives a maximal description of the conformational diversity of a molecule when only a subset of available conformers is used. A pre-computed search per compound record gives immediate access to a set of 3-D similar compounds (called "Similar Conformers") in PubChem and their respective superpositions. Systematic augmentation of PubChem resources to include a 3-D layer provides users with new capabilities to search, subset, visualize, analyze, and download data.</p> <p>A series of retrospective studies help to demonstrate important connections between chemical structures and their biological function that are not obvious using 2-D similarity but are readily apparent by 3-D similarity.</p> <p>Conclusions</p> <p>The addition of PubChem3D to the existing contents of PubChem is a considerable achievement, given the scope, scale, and the fact that the resource is publicly accessible and free. With the ability to uncover latent structure-activity relationships of chemical structures, while complementing 2-D similarity analysis approaches, PubChem3D represents a new resource for scientists to exploit when exploring the biological annotations in PubChem.</p

The [10-10] edge dislocation in the wurtzite structure: A high-resolution transmission electron microscopy investigation of [0001] tilt grain boundaries in GaN and ZnO

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Evolution of Secondary α Phase during Aging Treatment in Novel near β Ti-6Mo-5V-3Al-2Fe Alloy

Evolution of secondary &#945; phase during aging treatment of a novel near &#946; titanium alloy Ti-6Mo-5V-3Al-2Fe(wt.%) was studied by OM, SEM, and TEM. Results indicated that size and distribution of secondary &#945; phase were strongly affected by aging temperature and time. Athermal &#969; phase formed after super-transus solution treatment followed by water quenching, and promoted nucleation of needle-like intragranular &#945; in subsequent aging process. When aged at 480 &#176;C, fine scaled intragranular &#945; with small inter-particle spacing precipitated within &#946; grains and high ultimate tensile strength above 1500 MPa was achieved. When the aging temperature increased, the size and inter-particle spacing of intragranular &#945; increased and made the strength reduce, but the ductility got improved. When aging temperature reached as high as 600 &#176;C, &#969; phase disappeared and intragranular &#945; coarsened obviously, resulting in serious decrease of strength. While mutually parallel Widmanst&#228;tten &#945; laths formed at the vicinity of &#946; grain boundaries and grew into the internal area of &#946; grains, and significant improvement of ductility was achieved. As the aging time increased from 4 h to 16 h at 600 &#176;C, the intragranular &#945; grew slightly and brought about minor change of mechanical properties

Energetic and Electronic Properties of (0001) Inversion Domain Boundaries in ZnO

International audienceIn this work, the eight possible configurations of (0001) inversion domain boundaries (IDBs) in wurtzite ZnO have been investigated systematically by first‐principle calculations based on density‐functional theory (DFT). The energetic stability revealed that H4 are the most stable among the Head‐to‐Head type (H) IDBs, whereas for the Tail‐to‐Tail type (T) IDBs, T1 and T2 IDBs have lower formation energies. Their electronic properties were investigated using the electron localization function (ELF) and the projected density of states (PDOS). The results revealed that all the boundaries present a metallic character with the hybridization bands crossing the Fermi level; they are mainly dominated by Zn:3d and O:2p states in H IDBs and Zn:4s states in T IDBs, respectively. In particular, owing to the polarization discontinuity, electron accumulation occurs at all the T IDB regions with the conduction band minimum (CBM) shifting down below the Fermi level