Spontaneous intraocular lens extrusion through Gundersen conjunctival graft after multiple failed keratoplasties: A report of two cases

Purpose: To report the occurrence and the outcomes of 2 cases of spontaneous extrusion of 3-piece intraocular lens (IOL) through a Gundersen conjunctival flap performed for tectonic purposes after multiple failed penetrating keratoplasties (PKs). Methods: A 70-year-old woman (Patient #1) with severe dry eye disease who had undergone cataract surgery with posterior chamber 3-piece IOL implantation, multiple PKs and Gundersen conjunctival flap in her left eye presented with partial extrusion of the optic and the inferior haptic of the IOL through the conjunctival flap. Inferior symblepharon and keratinization of the entire ocular surface were also present. A 64-year-old diabetic man (Patient #2) with history of cataract surgery with a posterior chamber IOL implant ation, multiple PKs for keratoconus and Gunderson conjunctival flap in his right eye presented with partial extrusion of IOL optic through the conjunctival flap. Results: Patient #1 refused a further surgery and IOL was removed at the slit lamp. Currently, 3 months after IOL extrusion, corneal perforation self-sealed, visual acuity of light perception is maintained, and the patient does not complain any symptoms of ocular discomfort or pain. In Patient #2, tectonic PK combined with IOL removal and anterior vitrectomy was performed. Currently, 1 month postoperatively corneal graft is clear, intraocular pressure is normal, and patient’s vision is counting fingers. Conclusions: In both cases, IOL extrusion occurred spont

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I. I NTRODUCTION M ODERN communication systems are continuously evolving to fulfill the growing users’ demand for faster transmission rate and data capacity, leading to broader signal bandwidth, high-order modulations, and high peak-to-average power ratio (PAPR). The requirements for the linearity of the transmission signal are more stringent, and higher energy

The power amplifiers (PAs) often suffer from load mismatch in mobile terminals due to the complicated working environments, making linearizing those PAs a challenging task. This article proposes a novel load-mismatch tracking digital predistortion (LMT-DPD) technique for linearizing mobile-terminal PAs under varying load-mismatch conditions. The time-domain polyharmonic distortion (TD-PHD) model is discussed first for the behavioral modeling and linearization of PA under mismatch, followed by a practical model construction method. Then, the proposed LMT-DPD technique is derived based on the superposition principle. According to the real-time load reflection coefficients, the LMT-DPD enables automatic and fast adjustments of the predistorter to adapt to the PA ’s nonlinear distortion under different load-mismatch conditions without coefficient recalibration. Experimental validations are carried out on a mobile-terminal PA module at 4.6 GHz with a 100-MHz 5G new radio (NR) signal. The test results show that the proposed LMTDPD technique can significantly reduce the update frequency of predistorter while realizing excellent linearization performance.

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I. I NTRODUCTION I MAGE denoising is a fundamental research task in low- level vision, which aims at restoring clean images from

Recently, self-supervised denoising methods have attracted significant attention due to the considerable challenge posed by constructing a large-scale real noise dataset for supervised training. The most representative self-supervised denoisers are based on blind-spot networks (BSNs), which exclude the central pixel of receptive field. However, excluding any input pixel potentially leads to the loss of vital information required for accurate predictions, especially when the excluded pixel corresponds to the output position. In addition, a standard BSN has struggled to effectively reduce real-world noise due to the spatial correlation of noise, though it makes the significant results with independently distributed synthetic noise. In this paper, we propose a nov el self-supervised real-world image denoising framework called Complementary-BSN based on two reciprocal branches (Mask-Map branch and Enhanced-PD-BSN branch) with an efficient loss function to employ the pixels information ignored by masked convolution and provide additional optimization target for self-supervised output. Specifically, we exploit a block-wise random-placing (BRP) scheme for further weaken the noisy correlation to avoid the illusion of image structure recovery due to existing complex noise and make Complementary-BSN more suitable for real noise. Additionally, we develop an efficient strategy (multi-stride PD (MPD)) to fuse multiple PD strides for inference, narrowing the restoration gap between textural and flat regions. Extensive experiments on real-world datasets demonstrate that our method achieves superior performance to other state-ofthe-art (SOTA) self-supervised denoising methods. The code is available at https://github.com/cuijin7382/Complementary-BSN.

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Multi-objective CH selection with Scheduling based energy efficient

In Wireless Sensor Networks (WSNs), energy efficiency, secure data transmission, and reduced latency are critical for enhancing network lifetime and reliability. Previously several optimization algorithms were used for optimal cluster head selection, but they do not provide sufficient results and still there are challenges in secure data transmission. To overcome these challenges this work is motivated. In this manuscript, a Multi-objective CH selection with Scheduling based energy efficient secure transmission in WSN using Siamese graph convolutional Mantis Search attention network (EST-SGCMSAN) is proposed. Initially, the cluster head is selected using the novel multi-objective CH selection strategy using the Red Piranha Alpine Skiing Optimization algorithm (RPASOA), designed to minimize ener gy usage and reduce transmission delay. Furthermore, an advanced sleep scheduling mechanism with duty cycling is introduced, leveraging a Siamese Graph Convolutional Mantis Search Attention Network (SGCMSAN) for reliable and energy-efficient scheduling. To address the security challenges inherent in sensor nodes, which often face constraints in processing power, storage, and energy, an AES-based signature generation approach utilizing White Box Cryptography (WBC) is implemented. This method ensures secure data transmission while maintaining low computational overhead. The proposed system aims to significantly extend the network lifetime of WSNs through optimized CH select

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Ni- and Sb-Doped SnO 2 Electrocatalysts with High Current Efficiency for Ozone Production via Electrodeposited Nanostructures

(Abstract not found)

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Food additives: From functions to analytical methods

Food additives refer to all kinds of trace substances used in food or food processing to preserve flavor or enhance food taste, appearance, or other qualities. At present, artificial synthetic food additives have gradually replaced the natural food additives and many problems related to food additives, involving the abuse of food additives, excessive additives or even toxic additives. Obviously, food additives can bring people great sensory enjoyment and commercial convenience, but they may also cause potential risks to human health. So, it is of high significance to conduct quantitative analysis on the content of food additives. According to their functions and the regulatory requirements of food additives, this review starts from the classification and structures of various food additives involving colorants, p reservatives, antioxidants, sweeteners, emulsifiers, stabilizers, thickeners, gelling agents. It then summarizes and discusses analytical methods for quantification of food additives including modern immunoassays and other biotechnological methods. The proposed review aspires to fill in the knowledge gap of food additives between academia and industry by covering all kinds of analytical methods for quantifying food additives. KEYWORDS analytical methods; food additives; food additives regulation; molecular structure and technique Introduction Food additives are substances added to food to preserve flavor or enhance its taste, appearance, or other qualities (I

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Molecular Recognition of VSIG1 in Biological Samples for Fast Diagnosis of Gastric Cancer

(Abstract not found)

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Nanoplatform-based analysis for the detection of HER3 and HER4 for gastric cancer diagnosis

Nanographene and α-cyclodextrin based sensors modified with gold nanoparticles and spheroidal copper were used to develop two stochastic sensors, which were then characterized and validated for the purpose of molecularly identifying and quantifying HER3 and HER4 in biological samples. In order to accomplish this goal, each of the stochastic sensors was incorporated in a nanoplatform. The two nanoplatforms were connected to a smartphone and recorded very low limits of determination (1 × 10−15 g ml−1) and wide linear concentration ranges (1 × 10−15–1 × 10−8 g ml−1) when a potential of 170 mV versus Ag/AgCl was applied. This allowed for the molecular identification and quantification of HER3 and HER4 in patients with gastric cancer, as well as in healthy ind ividuals. Keywords: stochastic sensor, nanoplatforms, gastric cancer, smartphone detection, HER (Some figures may appear in colour only in the online journal) 1. Introduction Human epidermal growth factor receptors, also known as ErbB, are engaged in crucial signaling pathways, including those that govern cell development, proliferation, and death [1, 2]. The HER family consists of four members: HER1 (EGFR or ErbB1), HER2 (ErbB2), HER3 (ErbB3), and HER4 (ErbB4) [3]. Overexpression of HER has been associated with the development of malignant potential and a bad prognosis in a number of different types of cancer [4, 5]. However, recent studies have proven that heterodimerization of HER3 with HER1/HER2/HER4 triggers a signaling n

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4SzoX7

(Abstract not found)

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