Miracle-dz/UniFAR-FTUs huggingface dataset:
Aggregation-Morphology-Dependent Electrochemical Performance of Co3O4 Anode Materials for Lithium-Ion Batteries

The aggregation morphology of anode materials plays a vital role in achieving high performance lithium-ion batteries. Herein, Co3O4 anode materials with different aggregation morphologies were successfully prepared by modulating the morphology of precursors with different cobalt sources by the mild coprecipitation method. The fabricated Co3O4 can be flower-like, spherical, irregular, and urchin-like. Detailed investigation on the electrochemical performance demonstrated that flower-like Co3O4 consisting of nanorods exhibited superior performance. The reversible capacity maintained 910.7 mAh·g−1 at 500 mA·g−1 and 717 mAh·g−1 at 1000 mA·g−1 after 500 cycles. The cyclic stability was greatly enhanced, with a capacity retention rate of 92.7% at 500 mA·g−1 and 78.27% at 1000 mA·g−1 after 500 cycles. Electrochemical performance in long-term storage and high temperature conditions was still excellent. The unique aggregation morphology of flower-like Co3O4 yielded a reduction of charge-transfer resistance and stabilization of electrode structure compared with other aggregation morphologies.
The Diverse Contributions of Fucose Linkages in Cancer

Fucosylation is a post-translational modification of glycans, proteins, and lipids that is responsible for many biological processes. Fucose conjugation via α(1,2), α(1,3), α(1,4), α(1,6), and O’- linkages to glycans, and variations in fucosylation linkages, has important implications for cancer biology. This review focuses on the roles that fucosylation plays in cancer, specifically through modulation of cell surface proteins and signaling pathways. How L-fucose and serum fucosylation patterns might be used for future clinical diagnostic, prognostic, and therapeutic approaches will be discussed.
Bug Detection and Localization Using Symbolic Quick Error Detection 1

We present Symbolic Quick Error Detection (Symbolic QED), a structured approach for logic bug detection and localization which can be used both during pre-silicon design verification as well as post-silicon validation and debug. This new methodology leverages prior work on Quick Error Detection (QED) which has been demonstrated to drastically reduce the latency, in terms of the number of clock cycles, of error detection following the activation of a logic (or electrical) bug. QED works through software transformations, including redundant execution and control flow checking, of the applied tests. Symbolic QED combines these errordetecting QED transformations with bounded model checking-based formal analysis to generate minimal-length bug activation traces that detect and localize any logic bugs in the design. We demonstrate the practicality and effectiveness of Symbolic QED using the OpenSPARC T2, a 500-million-transistor open-source multicore System-on-Chip (SoC) design, and using "difficult" logic bug scenarios observed in various state-of-the-art commercial multicore SoCs. Our results show that Symbolic QED: (i) is fully automatic, unlike manual techniques in use today that can be extremely time-consuming and expensive; (ii) requires only a few hours in contrast to manual approaches that might take days (or even months) or formal techniques that often take days or fail completely for large designs; and (iii) generates counter-examples (for activating and detecting logic bugs) that are up to 6 orders of magnitude shorter than those produced by traditional techniques. Significantly, this new approach does not require any additional hardware.
Structure-Preserving 3D Garment Modeling with Neural Sewing Machines

3D Garment modeling is a critical and challenging topic in the area of computer vision and graphics, with increasing attention focused on garment representation learning, garment reconstruction, and controllable garment manipulation, whereas existing methods were constrained to model garments under specific categories or with relatively simple topologies. In this paper, we propose a novel Neural Sewing Machine (NSM), a learning-based framework for structure-preserving 3D garment modeling, which is capable of learning representations for garments with diverse shapes and topologies and is successfully applied to 3D garment reconstruction and controllable manipulation. To model generic garments, we first obtain sewing pattern embedding via a unified sewing pattern encoding module, as the sewing pattern can accurately describe the intrinsic structure and the topology of the 3D garment. Then we use a 3D garment decoder to decode the sewing pattern embedding into a 3D garment using the UV-position maps with masks. To preserve the intrinsic structure of the predicted 3D garment, we introduce an inner-panel structure-preserving loss, an inter-panel structure-preserving loss, and a surface-normal loss in the learning process of our framework. We evaluate NSM on the public 3D garment dataset with sewing patterns with diverse garment shapes and categories. Extensive experiments demonstrate that the proposed NSM is capable of representing 3D garments under diverse garment shapes and topologies, realistically reconstructing 3D garments from 2D images with the preserved structure, and accurately manipulating the 3D garment categories, shapes, and topologies, outperforming the state-of-the-art methods by a clear margin.
Functional demonstration of plant flavonoid carbocations proposed to be involved in the biosynthesis of proanthocyanidins.

The plant flavonoid dogma proposes that labile plant flavonoid carbocations (PFCs) play vital roles in the biosynthesis of proanthocyanidins (PAs). However, whether PFCs exist in plants and how PFCs function remain unclear. Herein, we report the use of an integrative strategy including enzymatic assays, mutant analysis, metabolic engineering, isotope labeling, and metabolic profiling to capture PFCs and demonstrate their functions. In anthocyanidin reductase (ANR) assays, an (-)-epicatechin conjugate was captured in protic polar nucleophilic methanol alone or methanol-HCl extracts. Tandem mass spectrum (MS/MS) analysis characterized this compound as an (-)-epicatechin-4-O-methyl (EOM) ether, which resulted from (-)-epicatechin carbocation and the methyl group of methanol. Acid-based catalysis of procyanidin B2 and B3 produced four compounds, which were annotated as two EOM and two (+)-catechin-4-O-methyl (COM) ethers. Metabolic profiling of seven PA pathway mutants showed an absence or reduction of two EOM ether isomers in seeds. Camellia sinensis ANRa (CsANRa), leucoanthocyanidin reductase c (CsLARc), and CsMYB5b (a transcription factor) were independently overexpressed for successful PA engineering in tobacco. The EOM ether was remarkably increased in CsANRa and CsMYB5b transgenic flowers. Further metabolic profiling for eight green tea tissues revealed two EOM and two COM ethers associated with PA biosynthesis. Moreover, an incubation of (-)-epicatechin or (+)-catechin with epicatechin carbocation in CsANRa transgenic flower extracts formed dimeric procyanidin B1 or B2, demonstrating the role of flavan-3-ol carbocation in the formation of PAs. Taken together, these findings indicate that flavan-3-ol carbocations exist in extracts and are involved in the biosynthesis of PAs of plants. This article is protected by copyright. All rights reserved.
Metamaterial architecture from a self-shaping carnivorous plant

Significance Understanding how carnivorous plants actuate leaf motion to capture their preys is an intriguing problem with potential applications for bio-inspired materials. Here, we show that bending of Drosera capensis leaves is programmed into its asymmetric cellular architecture and actuated by the action of growth hormones. A similar strategy can be exploited to design shape-changing metamaterials converting homogeneous stimuli into an asymmetric response. As meticulously observed and recorded by Darwin, the leaves of the carnivorous plant Drosera capensis L. slowly fold around insects trapped on their sticky surface in order to ensure their digestion. While the biochemical signaling driving leaf closure has been associated with plant growth hormones, how mechanical forces actuate the process is still unknown. Here, we combine experimental tests of leaf mechanics with quantitative measurements of the leaf microstructure and biochemistry to demonstrate that the closure mechanism is programmed into the cellular architecture of D. capensis leaves, which converts a homogeneous biochemical signal into an asymmetric response. Inspired by the leaf closure mechanism, we devise and test a mechanical metamaterial, which curls under homogeneous mechanical stimuli. This kind of metamaterial could find possible applications as a component in soft robotics and provides an example of bio-inspired design.
Identification of Msp1-Induced Signaling Components in Rice Leaves by Integrated Proteomic and Phosphoproteomic Analysis

MSP1 is a Magnaporthe oryzae secreted protein that elicits defense responses in rice. However, the molecular mechanism of MSP1 action is largely elusive. Moreover, it is yet to be established whether MSP1 functions as a pathogen-associated molecular pattern (PAMP) or an effector. Here, we employed a TMT-based quantitative proteomic analysis of cytosolic as well as plasma membrane proteins to decipher the MSP1 induced signaling in rice. This approach led to the identification of 6691 proteins, of which 3049 were identified in the plasma membrane (PM), while 3642 were identified in the cytosolic fraction. A parallel phosphoproteome analysis led to the identification of 1906 phosphopeptides, while the integration of proteome and phosphoproteome data showed activation of proteins related to the proteolysis, jasmonic acid biosynthesis, redox metabolism, and MAP kinase signaling pathways in response to MSP1 treatment. Further, MSP1 induced phosphorylation of some of the key proteins including respiratory burst oxidase homologue-D (RBOHD), mitogen-activated protein kinase kinase kinase-1 (MEKK1), mitogen-activated protein kinase-3/6 (MPK3/6), calcium-dependent protein kinase (CDPK) and calmodulin (CaM) suggest activation of PAMP-triggered immunity (PTI) in response to MSP1 treatment. In essence, our results further support the functioning of MSP1 as a PAMP and provide an overview of the MSP1 induced signaling in rice leaves.
Dual-polarized Grid-slotted Microstrip Antenna with Enhanced Bandwidth and Low Profile

─ In this paper, a wideband dual-polarized microstrip antenna is proposed inspired by the concept of the periodic grid-slotted surface. Inherited the merit of low profile from microstrip antenna, a relatively wide bandwidth, i.e., up to 43% with reflection coefficient less than -10 dB, is achieved for both orthogonal polarizations. Multiple modes are excited due to the periodic slotloaded structure and well matched using crossed aperture-coupled Y-shaped feeding. The overall height of the proposed antenna is only 0.06 λ0, λ0 is the freespace wavelength at the centre frequency of 5.5 GHz. The proposed antenna is systematically studied in the numerical simulation and experiment, and results agree well. The proposed dual-polarized antenna provides an effective choice for wideband low profile antenna for the applications of base station. Index Terms ─ Antenna feeds, microstrip antennas, polarization diversity, wideband antennas.
Sex differences in crude mortality rates and predictive value of intensive care unit-based scores when applied to the cardiac intensive care unit

Background: Limited data exists regarding sex differences in outcome and predictive accuracy of intensive care unit-based scoring systems when applied to cardiac intensive care unit patients. Methods: We reviewed medical records of patients admitted to cardiac intensive care unit from 1 January 2011–31 December 2016. Sex differences in mortality rates and the performance of intensive care unit-based scoring systems in predicting in-hospital mortality were analyzed. Calibration was assessed by the Hosmer-Lemeshow test and locally weighted scatterplot smoothing curves. Discrimination was assessed using the c statistic and receiver-operating characteristic curve. Results: Among 6963 patients, 2713 (39%) were women. Overall in-hospital and cardiac intensive care unit mortality rates were similar in women and men (9.1% vs 9.4%, p=0.67 and 5.9% vs 6%, p=0.88, respectively) and in age and major diagnosis subgroups. Of the scoring systems, Acute Physiology and Chronic Health Evaluation III and Sequential Organ Failure Assessment had poor calibration (Hosmer-Lemeshow p value <0.001), while Simplified Acute Physiology Score II performed better (Hosmer-Lemeshow p value 0.09), in both women and men. All scores had good discrimination (C statistics >0.8). In the subgroups of acute myocardial infarction and heart failure patients, all scores had good calibration (Hosmer-Lemeshow p>0.001) and discrimination (C statistic >0.8) while in diagnosis subgroups with highest mortality, the calibration varied among scores and by sex, and discrimination was poor. Conclusions: No sex differences in mortality were seen in cardiac intensive care unit patients. The mortality predictive value of intensive care unit-based scores is limited in both sexes and variable among different subgroups of diagnoses.
Selecting slip resistant winter footwear for personal support workers

BACKGROUND: Falls are among the leading causes of occupational injuries for workers exposed to outdoor winter conditions such as Personal Support Workers (PSWs). Slip resistant footwear is known to reduce the risk of falls, however, it is difficult to predict how well a particular boot will perform prior to purchasing them. Our recently developed Maximum Achievable Angle (MAA) test can be used to rate footwear objectively to address this gap. OBJECTIVE: To rate the slip resistance of a selection of winter footwear that meets the needs and preferences of PSWs. METHODS: We selected 40 representative types of footwear based on survey results from 677 PSWs and applied our MAA test to rate slip resistance. RESULTS: Comfort and slip resistance were rated the most important features for selecting winter footwear. Of the 40 types of footwear tested, six were found to have a good slip resistance on ice. CONCLUSION: The vast majority of winter footwear that meet the needs and preferences of PSWs, perform poorly on ice. Therefore, PSWs should consult our website (ratemytreads.com) for selecting appropriate footwear that will keep them safe in the winter.
Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning

Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.
Visualizing social support in home pediatric palliative care using network maps

Background: Home care of children with life-limiting diseases is extremely challenging for parents/family caregivers and their social environment. In order to gain new insights into the perspective of family caregivers, we employed digital Network Maps for the first time in the field of pediatric palliative care. Aim: To examine whether the use of Network Maps helps to identify and visualize significant members of the social support system and the quality of the relationship, as well as the main areas of life that are experienced as being supportive by each individual. Design: The design was an integrated mixed methods study. Creation of Network Maps was assessed in conjunction with qualitative interviews. In addition, participants gave an oral feedback on the Network Maps themselves. Setting/participants: Parents of patients supported by a Specialized Home Pediatric Palliative Care team were eligible for inclusion. Forty-five parents were enrolled in the study. Results: All mothers and fathers were able to generate their individual Network Map without problems. The composition of the support systems differed greatly, even between members of the same family. Parents named on average 11 supporting actors, mainly family members and health care professionals. Some relationships were perceived as helpful and stressful at the same time. Conclusion: Network Maps appear to be an appropriate tool for the collection, reconstruction, and assessment of the current support situation of parents of dying children. Further studies should examine the usefulness of Network Maps for the understanding of the caregivers’ support needs and for the development of psychosocial interventional strategies by pediatric palliative care teams.
Comparison of albumin-bilirubin grade, platelet-albumin-bilirubin grade and Child-Turcotte-Pugh class for prediction of survival in patients with large hepatocellular carcinoma after transarterial chemoembolization combined with microwave ablation

Abstract Purpose: To compare the predictive value of albumin-bilirubin (ALBI) grade, platelet-ALBI (PALBI) grade and Child-Turcotte-Pugh (CTP) class in patients with large hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE) combined with microwave ablation (TACE-MWA). Methods: A total of 349 consecutive HCC patients (89.1% male; mean [± SD] age 53.4 ± 12.27 years) from three medical centers, who underwent TACE-MWA for up to 3 HCCs with maximum diameters of 5.1–8.0 cm between January 2000 and June 2018, were investigated. Overall survival (OS) and progression-free survival (PFS) were analyzed. The prognostic performances of ALBI grade, PALBI grade and CTP class were compared. Results: TACE procedures were performed using lobaplatin (20–50 mg), epirubicin (30–60 mg), lipiodol (5–25 mL) and gelatin sponge particles (350–560 μm). The end point of the TACE procedure was stasis of blood flow in the feeder artery. The median follow-up duration was 28.0 months, the median OS was 28.0 months (95% confidence interval [CI] 23.55–32.45 months), and the median PFS was 4.8 months (95% CI 4.26–5.34 months). Patients with a ablation margin size of 11–15 mm experienced better PFS than those with a margin size of 6–10 or 0–5 mm (median, 6.5 versus [vs] 4.0 vs 2.3 months; p < .001). PALBI grade demonstrated significantly greater area under the curve values than ALBI grade or CTP class in predicting 1-, 3- and 5-year OS. Conclusions: PALBI grade provided better predictive value than ALBI grade or CTP class in patients with large HCCs after TACE-MWA.
Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs

Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93.
Mutant Huntingtin Affects Diabetes and Alzheimer’s Markers in Human and Cell Models of Huntington’s Disease

A higher incidence of diabetes was observed among family members of individuals affected by Huntington’s Disease with no follow-up studies investigating the genetic nature of the observation. Using a genome-wide association study (GWAS), RNA sequencing (RNA-Seq) analysis and western blotting of Rattus norvegicus and human, we were able to identify that the gene family of sortilin receptors was affected in Huntington’s Disease patients. We observed that less than 5% of SNPs were of statistical significance and that sortilins and HLA/MHC gene expression or SNPs were associated with mutant huntingtin (mHTT). These results suggest that ST14A cells derived from R. norvegicus are a reliable model of HD, since sortilins were identified through analysis of the transcriptome in these cells. These findings help highlight the genes involved in mechanisms targeted by diabetes drugs, such as glucose transporters as well as proteins controlling insulin release related to mHTT. To the best of our knowledge, this is the first GWAS using RNA-Seq data from both ST14A rat HD cell model and human Huntington’s Disease.
The Association Between the Ratio of Fibrinogen to Albumin With Contrast-Induced Nephropathy After Carotid Angiography: Reply

We thank Ozcan Cetin et al for their interest in our article. Stated in their letter, we found a relationship between fibrinogen to albumin ratio and contrast-induced nephropathy (CIN) in patients who underwent carotid angiography. Blood viscosity is a major determinant of blood flow. Whole blood viscosity is determined by the complex interactions of blood constituents, including red blood cells, white blood cells, platelets, fibrinogen, and other plasma proteins, such as albumin. An increase in whole blood viscosity was found to be a predictor for various cardiovascular disease. There may be an association between increased fibrinogen to albumin ratio and hemoconcentration. We did not measure blood viscosity. The European Society of Cardiology guidelines recommend to assess the total contrast medium volume to glomerular filtration rate ratio to predict the risk of CIN. However, this ratio was not calculated in our study. A decrease in albumin level is more frequent in heart failure. These patients have a high risk for the development of CIN. Unfortunately, we did not evaluate these patients separately in terms of CIN. We agree that it would be useful to evaluate whole blood viscosity for predicting CIN in patients who underwent carotid angiography.