Efficacy of Ultrasound-guided Radiofrequency Ablation of Parathyroid Hyperplasia: Single Session vs. Two-Session for Effect on Hypocalcemia

To evaluate safety and efficacy of one- vs. two-session radiofrequency ablation (RFA) of parathyroid hyperplasia for patients with secondary hyperparathyroidism (SHPT) and to compare the outcome of both methods on hypocalcemia. Patients with secondary hyperparathyroidism underwent ultrasound guided RFA of parathyroid hyperplasia. Patients were alternately assigned to either group 1 (n = 28) with RFA of all 4 glands in one session or group 2 (n = 28) with RFA of 2 glands in a first session and other 2 glands in a second session. Serum parathyroid hormone (PTH), calcium, phosphorus and alkaline phosphatase (ALP) values were measured at a series of time points after RFA. RFA parameters, including operation duration and ablation time and hospitalization length and cost, were compared between the two groups. Mean PTH decreased in group 1 from 1865.18 ± 828.93 pg/ml to 145.72 ± 119.27 pg/ml at 1 day after RFA and in group 2 from 2256.64 ± 1021.72 pg/ml to 1388.13 ± 890.15 pg/ml at 1 day after first RFA and to 137.26 ± 107.12 pg/ml at 1 day after second RFA. Group 1\u27s calcium level decreased to 1.79 ± 0.31 mmol/L at day 1 after RFA and group 2 decreased to 1.89 ± 0.26 mmol/L at day 1 after second session RFA (P \u3c 0.05). Multivariate analysis showed that hypocalcemia was related to serum ALP. Patients with ALP ≥ 566 U/L had lower calcium compared to patients with ALP \u3c 566 U/L up to a month after RFA (P \u3c 0.05). Group 1\u27s RFA time and hospitalization were shorter and had lower cost compared with Group 2. US-guided RFA of parathyroid hyperplasia is a safe and effective method for treating secondary hyperparathyroidism. Single-session RFA was more cost-effective and resulted in a shorter hospital stay compared to two sessions. However, patients with two-session RFA had less hypocalcemia, especially those with high ALP

Muckenhoupt-type weights and the intrinsic structure in Bessel Setting

Fix $\lambda>-1/2$ and $\lambda \not=0$. Consider the Bessel operator (introduced by Muckenhoupt--Stein) $\triangle_\lambda:=-\frac{d^2}{dx^2}-\frac{2\lambda}{x} \frac d{dx}$ on $\mathbb{R_+}:=(0,\infty)$ with $dm_\lambda(x):=x^{2\lambda}dx$ and $dx$ the Lebesgue measure on $\mathbb{R_+}$. In this paper, we study the Muckenhoupt-type weights which reveal the intrinsic structure in this Bessel setting along the line of Muckenhoupt--Stein and Andersen--Kerman. Besides, exploiting more properties of the weights $A_{p,\lambda}$ introduced by Andersen--Kerman, we introduce a new class $\widetilde{A}_{p,\lambda}$ such that the Hardy--Littlewood maximal function is bounded on the weighted $L^p_w$ space if and only if $w$ is in $\widetilde A_{p,\lambda}$. Moreover, along the line of Coifman--Rochberg--Weiss, we investigate the commutator $[b,R_\lambda]$ with $R_\lambda:=\frac{d}{dx}(\triangle_\lambda)^{-\frac{1}{2}}$ to be the Bessel Riesz transform. We show that for $w\in A_{p,\lambda}$, the commutator $[b, R_\lambda]$ is bounded on weighted $L^p_w$ if and only if $b$ is in the BMO space associated with $\triangle_\lambda$.Comment: 30 page

N,N′-Bis(pyridin-3-yl)terephthalamide–terephthalic acid (1/1)

In the title compound, C18H14N4O2·C8H6O4, both types of mol­ecule lie on inversion centers. In the N,N′-bis­(pyridin-3-yl)terephthalamide mol­ecule, the pyridine ring forms a dihedral angle of 11.33 (9)° with the central benzene ring. In the crystal, N—H⋯O and O—H⋯N hydrogen bonds connect the components into a three-dimensional network

Shadow-Aware Dynamic Convolution for Shadow Removal

With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multimedia tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap between the color mappings for the shadow region and the non-shadow region, leading to poor quality of reconstructed images and a heavy computation burden. To solve this problem, this paper introduces a novel plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction. Given that the non-shadow region often contains more background color information, we further develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region. Extensive experiments on the ISTD and SRD datasets show our method achieves better performance in shadow removal over many state-of-the-arts. Our code is available at https://github.com/xuyimin0926/SADC

Cognition driven framework for improving collaborative working in construction projects: Negotiation perspective

Negotiation is the popular collaborative decision‐making behavior in inter‐organization systems, especially in the collaborative working in construction projects (CWCP). However, negotiation has long been recognized as a critical but time‐ and energy‐consuming process. The lack of an effective framework to improve the efficiency (performance) of negotiation is a major problem for those seeking to enhance the efficiency and effectiveness of collaborative working in construction projects. This paper aims to develop a cognitive mapping‐based application framework for improving collaborative working in construction project from negotiation perspective (CF‐CWCP). This framework includes two‐fold: (1) mapping negotiation process in construction projects using cognitive mapping technique; (2) developing CF‐CWCP by integrating intelligent agent and cognitive mapping techniques. This research will benefit the partners in construction projects to improve construction negotiation performance. A prototype of CF‐CWCP is developed. Santrauka Derybos yra populiarus bendradarbiavimu gristas tarimasis tarp organizaciniu sistemu priimti sprendi‐mus, ypač vykdant statybu projektus. Derybos jau seniai suvokiamos kaip vertingas, tačiau daug laiko ir energijos atimantis procesas. Veiksmingos sistemos, galinčios padeti pagerinti derybu efektyvuma, trūku‐mas yra viena iš pagrindiniu problemu siekiantiems padidinti bendradarbiavimo veiksminguma vykdant statybos projektus. Pagrindinis šio straipsnio tikslas ‐ išpletoti pažinimo kartografija paremtos sistemos, kuri pagerintuben‐dradarbiavima vykdant statybos projektus, taikyma atsižvelgiant i derybu perspektyvas. Šia sistema suda‐ro dvi dalys: 1) kartografinis derybu procesas vykdant statybos projektus, pagristas pažinimo kartografijos technologija; 2) pažinimo sistemos, gerinančios bendradarbiavima vykdant statybos projektus, pletojimas integruojant intelektinius agentus ir pažinimo kartografijos technologija. Šis tyrimas pades statybu projek‐tu dalyviams pagerinti derybu efektyvuma, be to, išpletotas pažinimo sistemos prototipas. First Published Online: 09 Jun 2011 Reikšminiai žodžiai: pažinimo kartografija, bendradarbiavimas, derybos, statybos projekta

Fine-grained Data Distribution Alignment for Post-Training Quantization

While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce images. To alleviate this limitation, in this paper, we leverage the synthetic data introduced by zero-shot quantization with calibration dataset and propose a fine-grained data distribution alignment (FDDA) method to boost the performance of post-training quantization. The method is based on two important properties of batch normalization statistics (BNS) we observed in deep layers of the trained network, (i.e.), inter-class separation and intra-class incohesion. To preserve this fine-grained distribution information: 1) We calculate the per-class BNS of the calibration dataset as the BNS centers of each class and propose a BNS-centralized loss to force the synthetic data distributions of different classes to be close to their own centers. 2) We add Gaussian noise into the centers to imitate the incohesion and propose a BNS-distorted loss to force the synthetic data distribution of the same class to be close to the distorted centers. By utilizing these two fine-grained losses, our method manifests the state-of-the-art performance on ImageNet, especially when both the first and last layers are quantized to the low-bit. Code is at \url{https://github.com/zysxmu/FDDA}.Comment: ECCV202