ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Structural and functional investigation of ABC transporter STE6-2p from Pichia pastoris reveals unexpected interaction with sterol molecules

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters are multidomain transmembrane proteins, which facilitate the transport of various substances across cell membranes using energy derived from ATP hydrolysis. They are important drug targets since they mediate decreased drug susceptibility during pharmacological treatments. For the methylotrophic yeast Pichia pastoris, a model organism that is a widely used host for protein expression, the role and function of its ABC transporters is unexplored. In this work, we investigated the Pichia ABC-B transporter STE6-2p. Functional investigations revealed that STE6-2p is capable of transporting rhodamines in vivo and is active in the presence of verapamil and triazoles in vitro. A phylogenetic analysis displays homology among multidrug resistance (MDR) transporters from pathogenic fungi to human ABC-B transporters. Further, we present high-resolution single-particle electron cryomicroscopy structures of an ABC transporter from P. pastoris in the apo conformation (3.1 Å) and in complex with verapamil and adenylyl imidodiphosphate (AMP-PNP) (3.2 Å). An unknown density between transmembrane helices 4, 5, and 6 in both structures suggests the presence of a sterol-binding site of unknown function.
A changing cultural climate: Realising the value of artists working in Antarctica

The ratification of the Antarctic Treaty established a unique construct for human presence and activity in Antarctica. The designation of the continent for peace and science has inspired and informed the work of artists from across the world. This paper explores relationships between the Treaty and contemporary visual artists' responses to Antarctica. Using data from interviews with scientists, cultural professionals and exhibition audiences, I explore the value to science and society of artists' presence in Antarctica. I look at why in the last 2 years the number of artists being supported to work in Antarctica has declined and conclude with some observations on how this downward trend might be addressed.
Video resolution enhancement by using discrete and stationary wavelet transforms with illumination compensation

This paper proposes a new video resolution enhancement technique, in which a state-of-the-art illumination compensation procedure is applied to the respective frames before the registration process. After illumination compensation process, the respective frames are registered by using Vandewalle technique. In parallel, the corresponding frame is decomposed into its frequency subbands by using discrete wavelet transform (DWT) and stationary wavelet transform (SWT). Furthermore, the high-frequency subbands (LH, HL, and HH) have been super resolved by using Vandewalle super resolution technique. Afterward, the super resolved high-frequency subbands are being enhanced by the ones obtained through SWT as the latter ones contain more information. The enhanced high-frequency subbands and the output of the registration technique, which is regarded as the low-frequency subband, have been combined by using inverse DWT (IDWT) in order to construct the high-resolution frame. The quantitative (PSNR) results show the superiority of the proposed technique over the conventional and state-of-the-art video resolution enhancement techniques, in which for Akiyo video sequence there are 5.45 dB improvements over the average PSNR compared to Vandewalle registration technique with structure adaptive normalized convolution.
Unintended consequences of heavy air pollution control: efficiency losses, resource misallocation, and firm innovation in China

Disentangling the heterogeneous effects of environmental regulations on firms' productivity and reducing the destructive effects of policies are important for achieving the goal of sustainable development. However, there is insufficient discussion of the micro-mechanisms of regulation. This study takes advantage of the good exogenous impact of China's first action plan, the Atmosphere Ten Articles, which has been formulated and implemented to solve prominent environmental problems, and constructs a difference-in-difference model based on the data of listed companies from 2012 to 2019. Results show that the policy generally has a significantly negative impact on the companies' TFP. This finding proved to be valid after a series of robustness tests, such as replacing the explained variables, eliminating the impact of penecontemporaneous policies, using propensity score matching, and applying instrumental variable methods. Due to the resource allocation mechanism, the policy undermines the companies' TFP, especially those in PM2.5 regulatory areas and state-owned companies. However, the strict emission reduction constraint instead pushes firms to innovate, and as a result, the policy's innovation mechanism promotes firms' productivity, which eventually offsets the negative effects arising from resource allocation distortions.
Development of beryllium-based neutron target system with three-layer structure for accelerator-based neutron source for boron neutron capture therapy

The iBNCT project team with University of Tsukuba is developing an accelerator-based neutron source. Regarding neutron target material, our project has applied beryllium. To deal with large heat load and blistering of the target system, we developed a three-layer structure for the target system that includes a blistering mitigation material between the beryllium used as the neutron generator and the copper heat sink. The three materials were bonded through diffusion bonding using a hot isostatic pressing method. Based on several verifications, our project chose palladium as the intermediate layer. A prototype of the neutron target system was produced. We will verify that sufficient neutrons for BNCT treatment are generated by the device in the near future.
Alternative antiviral approaches to combat influenza A virus

Influenza A (IAV) is a major human respiratory pathogen that contributes to a significant threat to health security, worldwide. Despite vaccinations and previous immunisations through infections, humans can still be infected with influenza several times throughout their lives. This phenomenon is attributed to the antigenic changes of hemagglutinin (HA) and neuraminidase (NA) proteins in IAV via genetic mutation and reassortment, conferring antigenic drift and antigenic shift, respectively. Numerous findings indicate that slow antigenic drift and reassortment-derived antigenic shift exhibited by IAV are key processes that allow IAVs to overcome the previously acquired host immunity, which eventually leads to the annual re-emergence of seasonal influenza and even pandemic influenza, in rare occasions. As a result, current therapeutic options hit a brick wall quickly. As IAV remains a constant threat for new outbreaks worldwide, the underlying processes of genetic changes and alternative antiviral approaches for IAV should be further explored to improve disease management. In the light of the above, this review discusses the characteristics and mechanisms of mutations and reassortments that contribute to IAV's evolution. We also discuss several alternative RNA-targeting antiviral approaches, namely the CRISPR/Cas13 systems, RNA interference (RNAi), and antisense oligonucleotides (ASO) as potential antiviral approaches against IAV.
The art museum as lab to re-calibrate values towards sustainable development

Innovation and change have become major issues on the agenda of commercial, not-for-profit, government and local public organisations alike and are considered to be essential for our future. More recently companies have begun to recognize that current business models based on an abundance of natural resources are no longer sustainable and as yet unplanned but fundamental social system change is required. The implementation of such radical change requires combined efforts across a range of disciplines and fields of knowledge, including interpretative and expressive forms of cultural communication such as art. Indeed, culture is at the heart of system change because it directly influences individual behaviour. Museums, concert halls, opera houses and others present art as ways to explore how to stay meaningful in a world as both individuals and members of collectives or groups. Given the highly dynamic ICT driven environment and an increasing pluralistic culture at a global level, these institutions have the potential to play a crucial role in preparing for change, introducing new social desires and managing fear. At the same time as this growing recognition of the need for system change, the financial pressures on publicly funded cultural institutions are increasing due to stressed public budgets. A way needs to be found in which private companies and cultural institutions can develop mutually beneficial systems of support in order to promote sustainable social change across the,board. This paper introduces a case study of a public cultural institution exploring its possible role inspiring and facilitating culture change towards sustainable development. It focuses on how an art museum can find ways to productively complement business innovation and sustainability agendas. Since 2004 the Van Abbemuseum, a publicly funded museum for contemporary art in Eindhoven, The Netherlands, has performed a broad variety of experiments in artistic programming and public mediation aiming to deepen and renew its purpose and meaning. The focus of this case study will be on a business model innovation project that was executed in 2014, developing the idea of "art as tool - museum as lab" facilitating sustainable social innovation. It had four steps: (1) Value Proposition, (2) Value Creation, (3) Value Capture and (4) Lead User test. This business approach was deliberately chosen to create a "learning by doing" environment. The Van Abbemuseum staff aimed to develop a new value creation approach, to learn the language and understand the thinking logic to effectively engage with the new target audience: (business) leaders, decision makers and change facilitators. (C) 2016 Elsevier Ltd. All rights reserved.
3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we proposea 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each imagemodality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of differentmodalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1 x 1 x 1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusionmechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-contextLA-GANsmodel to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.
Caregivers to older adults require support: A scoping review of their priorities

The vast majority of older adults who are chronically ill rely on informal caregivers for support. Caregivers often require additional support to facilitate their role. To the best of our knowledge, there has yet to be a collation of caregiver-identified priorities for support. Using existing research, this scoping review provides a comprehensive picture of what caregivers have indicated as priorities for support. Arksey and O'Malley's scoping review framework guides this review. We searched MEDLINE, CINAHL and PsycINFO databases on July 2, 2021. We selected databases based on their relevance to nursing, health and social science. Inclusion criteria were peer-reviewed research of any design, a sample population of caregivers to older adults (>55 years), manuscripts published in English and the priorities for caregiver support identified by caregivers themselves. We screened a total of 3591 records, and 33 articles met the inclusion criteria. These studies were from geographic settings across the globe and used various quantitative, qualitative and mixed-method study designs. In our synthesis, we quantified the identified priorities within the studies using coding and content analysis. We present the following list of caregiver-identified priorities: (1) orientation to the caregiving role; (2) self-care and respite; (3) adapting healthcare; (4) improved supports; (5) information needs; (6) access to resources; (7) financial assistance. Policymakers, healthcare professionals and non-profit organisations can use evidence from this review to guide decisions when developing support services and interventions for caregivers.
A CMOS Magnetoresistive Sensor Front-End With Mismatch-Tolerance and Sub-ppm Sensitivity for Magnetic Immunoassays

Magnetic biosensing is an emerging technique for ultra-sensitive point-of-care (PoC) biomolecular detection. However, the large baseline-to-signal ratio and sensor-to-sensor mismatch in magnetoresistive (MR) biosensors severely complicates the design of the analog front-end (AFE) due to the high dynamic range (DR) required. The proposed AFE addresses these issues through new architectural and circuit level techniques including fast settling duty-cycle resistors (DCRs) to reduce readout time and a high frequency interference rejection (HFIR) sampling technique embedded in the ADC to relax the DR requirement. The AFE achieves an input-referred noise of 46.4 nT/root Hz, an input-referred baseline of less than 0.235 mT, and a readout time of 11 ms while consuming just 1.39 mW. Implemented in a 0.18 m CMOS process, this work has state-of-the-art performance with 22.7 faster readout time, > 7.8 lower baseline, and 2.3 lower power than previously reported MR sensor AFEs.
A High-Performance Dual-Topology CMOS Rectifier With 19.5-dB Power Dynamic Range for RF-Based Hybrid Energy Harvesting

This brief reports a dual-topology CMOS rectifier with an extended power dynamic range (PDR) for radio frequency (RF)-based hybrid energy harvesting (RF-HEH) systems. By leveraging both the cross-coupled differential drive (CCDD) and the Dickson topologies with high forward conduction and low reverse leakage, we obtain an extension of the rectifier's PDR by adaptively disabling the CCDD counterpart and enabling the Dickson counterpart to dominate the rectifier's performance during high-power operation. Apart from that, we formulate a rectifier-performance index (RPI), which accounts for the power conversion efficiency (PCE), the PDR, the sensitivity, and the load resistance of the rectifier to provide an adequate performance benchmark with the state-of-the-art rectifiers. Fabricated in a 130-nm CMOS, the proposed dual-topology rectifier measures a wide PDR of 19.5 dB with a peak PCE of 78.4% for a 100-kO load operating at 900 MHz. Besides, our prototype records the highest RPI of 19.2 compared to the recent arts operating at GSM900.
Visual tracking based on edge field with object proposal association

In this paper, we present a novel tracking system based on edge-based object proposal and data association called object proposal association. Our object proposal method accurately detects and localizes objects in an image by searching for object-like regions, with the assumption that an object is represented by a closed boundary. To search for closed boundaries in an image, we present a new Edge Fields (EFs) technique. Using this technique, our method can extract high-quality edges and can obtain accurate boundaries from the image. The EFs technique consists of blurring and thresholding steps, where the former helps extract high-quality edges and the latter prevents the method from losing image details while blurring. After the method extracts object-like regions, we associate the regions in the previous frame with those in the current frame. For this purpose, using the Markov chain Monte Carlo data association (MCMCDA) algorithm, we can find pairs of similar regions across two frames. Experimental results demonstrate that our object proposal method is competitive with state-of-the-art object proposal methods on the PASCAL VOC 2007 dataset. Our tracking method is also competitive with state-of-the-art tracking methods on Object Tracking Benchmark dataset. (C) 2017 Elsevier B.V. All rights reserved.
A Modified Similarity Metric for Unit Testing of Object-Oriented Software Based on Adaptive Random Testing

Finding an effective method for testing object-oriented software (OOS) has proven elusive in the software community due to the rapid development of object-oriented programming (OOP) technology. Although significant progress has been made by previous studies, challenges still exist in relation to the object distance measurement of OOS using Adaptive Random Testing (ART). This is partly due to the unique features of OOS such as encapsulation, inheritance and polymorphism. In a previous work, we proposed a new similarity metric called the Object and Method Invocation Sequence Similarity (OMISS) metric to facilitate multi-class level testing using ART. In this paper, we broaden the set of models in the metric (OMISS) by considering the method parameter and adding the weight in the metric to develop a new distance metric to improve unit testing of OOS. We used the new distance metric to calculate the distance between the set of objects and the distance between the method sequences of the test cases. Additionally, we integrate the new metric in unit testing with ART and applied it to six open source subject programs. The experimental result shows that the proposed method with method parameter considered in this study is better than previous methods without the method parameter in the case of the single method. Our finding further shows that the proposed unit testing approach is a promising direction for assisting software engineers who seek to improve the failure-detection effectiveness of OOS testing.
Cooperative assembly of filopodia by the formin FMNL2 and I-BAR domain protein IRTKS

Filopodia are long finger-like actin-based structures that project out from the plasma membrane as cells navigate and explore their extracellular environment. The initiation of filopodia formation requires release of tension at the plasma membrane followed by the coordinated assembly of long unbranched actin filaments. Filopodia growth is maintained by a tip complex that promotes actin polymerization and protects the growing barbed ends of the actin fibers from capping proteins. Filopodia growth also depends on additional F-actin bundling proteins to stiffen the actin filaments as well as extension of the membrane sheath projecting from the cell periphery. These activities can be provided by a number of actin-binding and membrane-binding proteins including formins such as formin-like 2 (FMNL2) and FMNL3, and Inverse-Bin-Amphiphysin-Rvs (I-BAR) proteins such as IRTKS and IRSp53, but the specific requirement for these proteins in filopodia assembly is not clear. We report here that IRTKS and IRSp53 are FMNL2-binding proteins. Coexpression of FMNL2 with either I-BAR protein promotes cooperative filopodia assembly. We find IRTKS, but not IRSp53, is required for FMNL2-induced filopodia assembly, and FMNL2 and IRTKS are mutually dependent cofactors in this process. Our results suggest that the primary function for FMNL2 during filopodia assembly is binding to the plasma membrane and that regulation of actin dynamics by its formin homology 2 domain is secondary. From these results, we conclude that FMNL2 initiates filopodia assembly via an unexpected novel mechanism, by bending the plasma membrane to recruit IRTKS and thereby nucleate filopodia assembly.
Multiple Roles of Alkanethiolate-Ligands in Direct Formation of H2 O2 over Pd Nanoparticles

Coadsorbed organic species including thiolates can promote direct synthesis of hydrogen peroxide from H2 and O2 over Pd particles. Here, density functional theory based kinetic modeling, augmented with activity measurements and vibrational spectroscopy are used to provide atomistic understanding of direct H2 O2 formation over alkylthiolate(RS) Pd. We find that the RS species are oxidized during reaction conditions yielding RSO2 as the effective ligand. The RSO2 ligand shows superior ability for proton transfer to the intermediate surface species OOH, which accelerates the formation of H2 O2 . The ligands promote the selectivity also by blocking sites for unselective water formation and by modifying the electronic structure of Pd. The work rationalizes observations of enhanced selectivity of direct H2 O2 formation over ligand-funtionalized Pd nanoparticles and shows that engineering of organic surface modifiers can be used to promote desired hydrogen transfer routes.
Residual RNN Models With Pruning for Digital Predistortion of RF Power Amplifiers

The paper presents several novel residual recurrent neural network (RNN) models for digital predistortion of radio-frequency power amplifiers. Then, unstructured and structured neural network (NN) pruning algorithms are proposed to reduce the per-sample computational complexity and the required memory, without compromising their modeling performance. The mathematical equations of the complexity and the memory are derived to analyze the hardware cost and design low cost models. An experimental platform is developed to measure the modeling performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error, error vector magnitude (EVM) and bit error rate. The platform uses an envelope tracking PA excited by 80 MHz IEEE 802.11ac signals. Compared with the state-of-the-art RNN model in (Sun et (a, 2019), the proposed models reduce the per-sample complexity and required memory by 94-99%, they also improve the ACLR and EVM by at least 3 dB and 1 dB at 0.45 dB and 3.15 dB gain compression, respectively. Furthermore, an observation path with time alignment is used to remove the transmit latency in the state-of-the-art approach. At 3.15 dB gain compression, the proposed models outperform the state-of-the-art feedforward and convolutional NN models by at least 1.3 dB in EVM, outperform the general memory polynomial model by at least 3.3 dB in EVM. The above evidences indicate that the proposed RNN models can be implemented with relatively low hardware cost and achieve the best EVM in high power efficiency scenario.