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Several clinical studies showed that statins were potential to treat polycystic ovary syndrome (PCOS). Through comprehensive search PubMed, EMBASE, the Web of Science, BIOSIS, the ClinialTrails.gov, and the Cochrane Library database up to 14 Feb 2020, we identified the randomized controlled trials about the treatment of statins on hyperandrogenism in PCOS women, and performed a systematic review and meta-analysis. The quality of the included studies was assessed by the Cochrane risk of bias tool and the Jadda score. Subgroup analysis and sensitivity analysis were conducted to analyze the pooled results. Nine trials included 682 PCOS patients were identified. Statins showed a significant potential to reduce testosterone (SMD = -0.47; 95% CI, − 0.76−− 0.18; P = 0.002) and dehydroepiandrosterone (SMD = -0.51; 95% CI, − 0.97−− 0.05; P = 0.03) levels, compared to the control treatments. The cutaneous symptoms hirsutism (SMD = -0.61; 95% CI, − 1.13−− 0.10; P = 0.02) and acne (SMD = -0.92; 95% CI, − 1.49−− 0.34; P = 0.002) were significantly improved by statins in PCOS women. Subgroup analysis showed that the two types of statins, and the different control treatments as well, presented no significantly different effect on testosterone and dehydroepiandrosterone. Sensitivity analysis confirmed the stability of the findings from the meta-analysis. In conclusion, statin treatment could significantly reduce androgen levels and improve cutaneous manifestations of hyperandrogenism of PCOS. [ABSTRACT FROM AUTHOR]

Affordable synthetic ammonia (NH3) enables the production of nearly half of the food we eat and is emerging as a renewable energy carrier. Sodium‐promoted chemical looping NH3 synthesis at atmospheric pressure using manganese (Mn) is here demonstrated. The looping process may be advantageous when inexpensive renewable hydrogen from electrolysis is available. Avoiding the high pressure of the Haber‐Bosch process by chemical looping using earth‐abundant materials may reduce capital cost, facilitate intermittent operation, and allow operation in geographic areas where infrastructure is less sophisticated. At this early stage, the data suggest that 0.28 m3 of a 50 % porosity solid Mn bed may suffice to produce 100 kg NH3 per day by chemical looping, with abundant opportunities for improvement. [ABSTRACT FROM AUTHOR]

Manganese (Mn) nitrides are important nitrogen (N) carriers for small‐scale intermittent ammonia (NH3) synthesis. However, only 3∼8 % of lattice N are converted into NH3. In this study, the geometric and electronic structures of well‐defined Mn4N and Mn2N lattices were altered using transition metal heteroatoms (Cr, Fe, Co, Ni, Mo) to understand the driving force behind lattice N diffusion and extraction. Density Functional Theory (DFT) revealed that the binding of early hydrogenation product (NH) follows a linear relationship with lattice N over a wide range of close‐packed surfaces. But, the binding of NH2 and NH3 are more sensitive to the geometric and electronic structures. Further, the chemical bonding can be quantitatively characterized with the covalency derived from Crystal Orbital Hamiltonian Population (COHP). In the Eley Rideal‐Mars van Krevelan pathway, the overall NH3 formation free energy (ΔGNH3) and lattice N diffusion barrier (Ea) are the respective thermodynamic and kinetic determining factors. Aided by a rate‐determining step (RDS) model, Mn4N modified with Fe, Co, Ni single‐atom dopants all show enhanced rates for NH3 formation. [ABSTRACT FROM AUTHOR]

• We take advantage of recent distributed word representation technology to help summarize text chunks. • The word embeddings in this research are utilized both in semantically and numerically. • We explore the relationships between natural language phrases in semantic view to extract latent knowledge domains. • Users' expertise is determined on historical performance, and data sparseness is alleviated by matrix factorization. • Evaluation is conducted on large scale dataset by comprehensive experiments, where preferable output is generated. Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users' expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence. [ABSTRACT FROM AUTHOR]

Author Yuanbiao Guo should be affiliated to: Department of Medical Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China. B Correction to: Reprod Biol Endocrinol 19, 189 (2021) b B https://doi.org/10.1186/s12958-021-00863-5 b Following publication of the original article [[1]], the authors reported an error to affiliation of author Yuanbiao Guo wherein affiliation 1 was inadvertently removed during correction stage. [Extracted from the article]

Convolutional neural networks (CNNs) have shown great performance in computer vision tasks, from image classification to pattern recognition. However, CNNs′${\rm CNNs}^{\prime }$ superior performance arises at the expense of high computational costs, which restricts their employment in real‐time decision‐making applications. Computationally intensive convolutions can be offloaded to optical metasurfaces, enabling sub‐picosecond latency and nearly zero energy consumption, but the currently reported approaches require additional bulk optics and can only process polarized light, which limits their practical usages in integrated lightweight systems. To solve these challenges, a novel design of the metasurface‐based optical convolutional accelerator is experimentally demonstrated, offering an ultra‐compact volume of 0.016 mm3${\rm mm}^{3}$, a low cross‐talk of ‐20 dB, polarization insensitivity, and is capable of implementing multiple convolution operations and extracting simultaneously various features from light‐encoded images. The ultra‐compact metasurface‐based optical accelerator can be compactly integrated with a digital imaging system to constitute an optical‐electronic hybrid CNN, which experimentally achieves a consistent accuracy of 96 % in arbitrarily polarized MNIST digits classification. The proposed ultra‐compact metasurface‐based optical convolutional accelerator paves the way for power‐efficient edge‐computing platforms for a range of machine vision applications. [ABSTRACT FROM AUTHOR]

Chirality, one of the universal phenomena in physics, forms the playground for fascinating phenomena in modern electromagnetism and industrial applications. Within the rapidly advancing technologies of integrated optoelectronic and all‐optical devices, controlling the light flow on a chip using optical chiral modes emerges as a crucial topic, which implies numerous counterintuitive chiroptical effects such as unidirectional emission, magnetic‐free non‐reciprocity, chiral switching, and enhanced sensitivity. Here strong yet reconfigurable mode chirality is demonstrated in integrated silicon‐based spiral microring resonators. Leveraging the adjustable azimuthal positions of two spiral edges as asymmetric local scatterers, the inter‐modal coupling can be manipulated, which bypasses the requirement of external off‐chip components in conventional schemes. Besides, an integrated phase shifter enables electrical reconfiguration of the non‐Hermiticity toward or away from exceptional points. Experimental results reveal post‐fabrication reconfiguration with a sign‐reversible chirality and chirality‐induced suppression of backscattering down to −24 dB. By virtue of demonstrations using standard silicon photonics foundry services, the findings provide a new design framework of microresonators as a building block for integrated chiral photonics in both classical and quantum regimes. [ABSTRACT FROM AUTHOR]

The Coreopsis tinctoria Nutt. flower (CTF) has been used traditionally in China for treating hypertension and diabetes as well as reducing body weight and blood fat. However, the vascular protection effect of the CTF has not been studied to date. This study aimed to screen and identify bioactive fractions from the CTF with a diabetic endothelial protection effect and to clarify the underlying mechanism. The vascular protection effect of Fraction A was studied in high-fat diet and streptozocin-induced diabetic models. The endothelial protection effect of Fraction A-2 was further studied in an in vitro vascular endothelial dysfunction model induced by high glucose. In a high glucose-induced human umbilical vein endothelial cell (HUVEC) model, Fractions A-2-2 and A-2-3 were screened, and their detailed mechanisms of endothelial protection were studied. Liquid chromatography mass spectrometry (LC-MS) was used to identify the main components in Fractions A-2-2 and A-2-3. Fraction A treatment significantly improved the endothelium-dependent vasodilation of the mesenteric artery induced by acetylcholine in diabetic rats. The maximum relaxation was 79.82 ± 2.45% in the control group, 64.36 ± 9.81% in the model group, and 91.87 ± 7.38% in the Fraction A treatment group (P < 0.01). Fraction A treatment also decreased rat tail pressure compared with the model group at the 12th week. The systolic blood pressure was 152.7 5 ± 16.99 mmHg in the control group, 188.50 ± 5.94 mmHg in the model group, and 172.60 ± 14.31 mmHg in the Fraction A treatment group (P < 0.05). The mean blood pressure was 128.50 ± 13.79 mmHg in the control group, 157.00 ± 6.06 mmHg in the model group, and 144.80 ± 11.97 mmHg in the Fraction A treatment group (P < 0.05). In an in vitro vascular endothelium-dependent vasodilation dysfunction model induced by high glucose, Fraction A-2 improved the vasodilation of the mesenteric artery. The maximum relaxation was 82.15 ± 16.24% in the control group, 73.29 ± 14.25% in the model group, and 79.62 ± 13.89% in the Fraction A-2 treatment group (P < 0.05). In a high glucose-induced HUVEC model, Fraction A-2-2 and Fraction A-2-3 upregulated the expression of IRS-1, Akt, and eNOS and increased the levels of p-IRS-1Ser307, p-Akt Ser473, and p-eNOSSer1177 and also decreased the expression of NOX4, TNF-α, IL-6, sVCAM, sICAM, and NF-κB (P < 0.01). With the intervention of AG490 and LY294002, the above effects of Fraction A-2-2 and Fraction A-2-3 were inhibited (P < 0.01). LC-MS data showed that in Fraction A-2-2 and Fraction A-2-3, there were 10 main components: flavanocorepsin; polyphenolic; flavanomarein; isochlorogenic acid A; dicaffeoylquinic acid; coreopsin; marein; coreopsin; luteolin-7-O-glucoside; and 3′,5,5',7-tetrahydroxyflavanone-O-hexoside. The protective effect of the CTF on diabetic endothelial dysfunction may be due to its effect on the JAK2/IRS-1/PI3K/Akt/eNOS pathway and the related oxidative stress and inflammation. The results strongly suggested that Fraction A-2-2 and Fraction A-2-3 were the active fractions from the CTF, and the CTF might be a potential option for the prevention of vascular complications in diabetes. Image 1 [ABSTRACT FROM AUTHOR]

Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject's EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user's proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings. [ABSTRACT FROM AUTHOR]

Recommender system for the IoT (RSIoT) has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence, and blockchain, RSIoT improves various indicators of residents' life. However, data integrity threats may affect the accuracy and consistency of the data particularly in the IoT environment where most devices are inherently dynamic and have limited resources that could fail in ensuring the quality of data transmission. Prior work has focused on processing big data and ensuring their integrity by considering cloud storage service as the popular way. In this article, we address integrity of data leveraging blockchain capabilities to ensure the integrity of the critical data. We adapted the Ethereum blockchain to our RCS for ensuring integrity of data during sharing them between doctor and patient without handling their data by third party. We build four smart contracts that enable our system of gaining more advantage of blockchain. We evaluated the performance of our smart contracts in Kovan and Rinkeby test networks. The preliminary results show the feasibility and effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices. [ABSTRACT FROM AUTHOR]

Background: Knee osteoarthritis (KOA) is a common disease based on degenerative pathological changes. Total knee arthroplasty (TKA) is an effective treatment for end-stage of KOA. However, only volume adaptation can be achieved with current knee prostheses, and it is difficult to achieve weight adaptation. This study focused on the weight difference of knee joints and initially explored the impact of this change on knee joint functional recovery and gait changes in patients after surgery.Methods: From October 2015 to June 2019, patients who underwent primary unilateral TKA were enrolled in this prospective cohort study with the same brand of knee prostheses. General data were collected from patients who met the criteria. The resected bone and soft tissues were collected and weighed precisely during TKA, and multivariate regression analysis was used to determine the factors affecting the weight of the removed knee tissues. We compared the weight of excised tissues and the total weight of the knee prosthesis, and the weight difference was defined as the increased weight of the knee joint (IWKJ). All patients were evaluated by HSS score, gait analysis, and affected side knee X-ray at two weeks, three months, and the last follow-up after the operation. To further determine the influence of IWKJ on postoperative functional recovery, the relationship between IWKJ, HSS score, and gait analysis was analyzed by univariate regression.Results: In total, 210 patients were eventually included in observation. All patients underwent postoperative follow-up for no less than two years. Multiple regression analysis showed that the course of the disease, body weight, and kellgren-Larencen stage(K-L stage)of the affected knee joint were independent factors affecting the weight of the removed knee tissues and were positively correlated with it. Univariate analysis showed that IWKJ was negatively correlated with HSS score at two weeks and three months after the operation. In addition, the values of spatiotemporal parameters and knee rotation ROM were negatively correlated with IWKJ two weeks after surgery, while outside food load response was positively correlated with IWKJ. Cadence, knee rotation ROM, and Ankle rotation ROM were negatively correlated with IWKJ, while outside food was positively correlated with IWKJ three months after surgery. At the last follow-up, only the hip rotation ROM was positively correlated with IWKJ.Conclusions: All Patients underwent TKA had varying degrees of increased knee weight. The increased weight was 298.98 ± 63.77 g. Patients' body weight, K-L staging, and disease duration are important factors that cause differences in resected knee tissue. Three months after the operation, the changes in knee joint weight had a negative correlation with the HSS score, which at the same time, it had varying degrees of linearity with gait parameters. However, the influence of weight diminished over time. [ABSTRACT FROM AUTHOR]

Abstract: The ordering behaviours of the transition metal elements in NbCr2-based alloys were predicted by combining thermodynamic model with ab-initio calculation. The predicted results show that for the ternary alloying elements in C15-type NbCr2-based alloy 64Cr–32Nb–4M (M = Cr, Hf, Mn, Mo, Nb, Ta, Ti, V, W or Zr), Mn atoms occupy only Cr sublattices (16d) and Hf, Ta, Ti and Zr atoms always prefer to occupy the Nb sublattices (8a). It is also found that the site preference of alloying elements Hf, Ta, Ti, Zr, or Mn are independent of the x Cr/x Nb (atomic ratio) and heat treatment temperature, while the site preferences of V, Mo or W depend on those. Present predicted results agree considerably with the available literatures, and some disagreements were also argued in this paper, i.e., in some literatures, alloying element V atoms always prefer to 16d sublattices and Ti atoms vary strongly with the alloy composition (x Cr/x Nb) and heat treatment temperature. [Copyright &y& Elsevier]

• We employ statistical analysis to define a list of quality features. • We propose a joint model by fusing autoencoder and random forest in an end-to-end trainable way for fake review detection. • Experimental results on the real dataset demonstrate the efficiency of the model. Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods. [ABSTRACT FROM AUTHOR]

The incidences of nodular goiter (NG), thyroid adenoma (TA), and thyroid cancer (TC) are increasing rapidly; however, the etiologies of these diseases remain unclear. The present study aimed to evaluate the differences in plasma fatty acids among these three thyroid diseases to facilitate etiological research. Four ω‑3 and seven ω‑6 polyunsaturated fatty acids were measured from 97 TC, 14 TA and 11 NG patient plasma samples with gas chromatography‑flame ionization detector. Fatty acids levels were expressed as the percentage of each fatty acid out of the total fatty acids evaluated. The present study identified that the level of 22:6n‑3 [median, interquartile range (IQR)] was significantly increased in TA (5.2%, 4.3‑6.4%) compared with NG (3.6%, 3.1‑4.6%) and TC patients (4.2%, 3.2‑4.8%). Though not statistically significant, the levels of 20:5n‑3 and 22:5n‑3 demonstrated a similar pattern. The level of 22:4n‑6 expressed (median, IQR) was significantly increased in NG patients (0.21%, 0.18‑0.26%) compared with TA (0.16%, 0.15‑0.18%) and TC (0.17%, 0.14‑0.22%) patients. Furthermore the fatty acids 18:3n‑6, 20:2n‑6, 20:3n‑6, 20:4:6, and 22:5n‑6 demonstrated a similar but statistically insignificant pattern. This suggests that different fatty acids exhibit various etiological roles in NG, TA and TC and warrant further study. [ABSTRACT FROM AUTHOR]