ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Fast Fixed-Outline 3-D IC Floorplanning With TSV Co-Placement

Through-silicon vias (TSVs) are used to connect inter-die signals in a 3-D IC. Unlike conventional vias, TSVs occupy device area and are very large compared to logic gates. However, most previous 3-D floorplanners only view TSVs as points. As a result, whitespace redistribution is necessary for TSV insertion after the initial floorplan is computed, which leads to suboptimal layouts. In this paper, we propose a very efficient 3-D floorplanner to simultaneously floorplan the functional modules and place the TSVs and to optimize the total wirelength under fixed-outline constraint. Compared to the state-of-the-art 3-D floorplanner with TSV planning, our design consistently produces better floorplans with 15% shorter wirelength and 31% fewer TSVs on average. Our algorithm is extremely fast and only takes a few seconds to floorplan benchmarks with hundreds of modules compared to hours as required by the previous state-of-the-art floorplanner.
Pain E-motion Faces Database (PEMF): Pain-related micro-clips for emotion research

A large number of publications have focused on the study of pain expressions. Despite the growing knowledge, the availability of pain-related face databases is still very scarce compared with other emotional facial expressions. The Pain E-Motion Faces Database (PEMF) is a new open-access database currently consisting of 272 micro-clips of 68 different identities. Each model displays one neutral expression and three pain-related facial expressions: posed, spontaneous-algometer and spontaneous-CO2 laser. Normative ratings of pain intensity, valence and arousal were provided by students of three different European universities. Six independent coders carried out a coding process on the facial stimuli based on the Facial Action Coding System (FACS), in which ratings of intensity of pain, valence and arousal were computed for each type of facial expression. Gender and age effects of models across each type of micro-clip were also analysed. Additionally, participants' ability to discriminate the veracity of pain-related facial expressions (i.e., spontaneous vs posed) was explored. Finally, a series of ANOVAs were carried out to test the presence of other basic emotions and common facial action unit (AU) patterns. The main results revealed that posed facial expressions received higher ratings of pain intensity, more negative valence and higher arousal compared with spontaneous pain-related and neutral faces. No differential effects of model gender were found. Participants were unable to accurately discriminate whether a given pain-related face represented spontaneous or posed pain. PEMF thus constitutes a large open-source and reliable set of dynamic pain expressions useful for designing experimental studies focused on pain processes.
A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single- channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single- channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.
Survey of State-of-the-Art Mixed Data Clustering Algorithms

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field.
The Changing Role of ENGOs in Water Governance: Institutional Entrepreneurs?

The changing role of the state in the last quarter century has been an important contemporary concern for policy makers, scholars, and the public. Equally, there is increasing recognition among governance scholars that nongovernment actors are exerting new kinds of influence over governance systems and contributing in novel ways to governance processes. The role of environmental nongovernmental organizations (ENGOs) is particularly pertinent given the continued involvement of ENGOs within collaborative, adaptive, and co-management governance, across several contexts and regions. This paper uses an analytical framework derived from recent studies on institutional entrepreneurs, to examine the skills ENGOs are applying in order to orchestrate change. An empirical case of governance for water in Canada's Lake Simcoe region provides the foundation for the research. Drawing on a mixed methods approach, the research finds that ENGOs in Lake Simcoe have taken on a role as an institutional entrepreneur, and thereby have altered the relationship between governance actors in this setting. A key outcome of their actions is a more dominant, engaged, and influential role for ENGOs in a critical, regional governance system.
Inhibition of LDL receptor-related protein 3 suppresses chondrogenesis of stem cells, inhibits proliferation, and promotes apoptosis

Articular cartilage defects remain the most common and challenging joint disease. Cartilage lacks the self-healing capacity after injury due to its avascularity. Recently, stem cell-based therapy has been applied for cartilage regeneration. However, the critical target for stem cells during chondrogenesis remains unclear. We first reported that LDL receptor-related protein 3 (LRP3) expression was markedly increased during chondrogenesis in stem cells. Furthermore, LRP3 was an effective chondrogenic stimulator, as confirmed by knockdown and overexpression experiments and RNA sequencing. In addition, inhibition of LRP3 suppressed proliferation and induced apoptosis. Therefore, our study first defined a new chondrogenic stimulator, LRP3, with detailed clarification, which provided a novel target for stem cell-based cartilage regeneration.
Stem cell-based models of early mammalian development

The complex process by which a single-celled zygote develops into a viable embryo is nothing short of a miraculous wonder of the natural world. Elucidating how this process is orchestrated in humans has long eluded the grasp of scientists due to ethical and practical limitations. Thankfully, pluripotent stem cells that resemble early developmental cell types possess the ability to mimic specific embryonic events. As such, murine and human stem cells have been leveraged by scientists to create in vitro models that aim to recapitulate different stages of early mammalian development. Here, we examine the wide variety of stem cell-based embryo models that have been developed to recapitulate and study embryonic events, from pre-implantation development through to early organogenesis. We discuss the applications of these models, key considerations regarding their importance within the field, and how such models are expected to grow and evolve to achieve exciting new milestones in the future.
Subcutaneous Administration of Medications and Fluids by Nonprofessional Caregivers at Home

Background: Patients requiring home-based palliative care have advanced complex illnesses with functional limitations and decline. This retrospective study reviewed caregiver administration of subcutaneous (SQ) medications and fluids when symptom control could not be achieved using the oral route. Methods: Medical records from September 1, 2017 to February 28, 2018 were reviewed for 272 consecutive patients who received SQ administration of medications or fluids at a home-based palliative care program. We analyzed the clinical characteristics of patients and caregivers, medications administered, and catheter outcomes. Results: Patients' median age was 74 years, and 163 (60%) were women. The most common cancer diagnoses were stomach 26 (12%), lung 22 (10%), and colorectal 20 (9%). Dementia 24 (44%), cerebrovascular disease 9 (16%), and congestive heart failure 7 (13%) were the most frequent nonmalignant diseases. Poor symptom control 162 (60%) and impaired oral intake 107 (39%) were the most common indications for an SQ route of administration. Nonprofessional caregivers trained by a nurse administered medications to 218 patients (80%). During interventions, the patients received a mean of 4 medications (±2 standard deviation). A total of 903 catheters were inserted, 15/732 (2%) catheters handled by nonprofessional caregivers caused a local infection, compared with 3/171 (1.8%) of catheters handled by nurses. Hydromorphone was the most common opioid used (57%), followed by morphine (35%). The median length of stay in the program was 24 days (interquartile range: 11-60). Conclusions: SQ administration of medications and fluids by nonprofessional caregivers trained by health care professionals is feasible and promising, but additional testing is needed.
Embedded Intelligence: State-of-the-Art and Research Challenges

Recent years have seen deployments of increasingly complex artificial intelligent (AI) and machine learning techniques being implemented on cloud server architectures and embedded into edge computing devices for supporting Internet of Things (IoT) and mobile applications. It is important to note that these embedded intelligence (EI) deployments on edge devices and cloud servers have significant differences in terms of objectives, models, platforms and research challenges. This paper presents a comprehensive survey on EI from four aspects: (1) First, the state-of-the-art for EI using a set of evaluation criteria is proposed and reviewed; (2) Second, EI for both cloud server accelerators and low-complexity edge devices are discussed; (3) Third, the various techniques for EI are categorized and discussed from the system, algorithm, architecture and technology levels; and (4) The paper concludes with the lessons learned and the future prospects are discussed in terms of the key role EI is likely to play in emerging technologies and applications such as Industry 4.0. This paper aims to give useful insights and future prospects for the developments in this area of study and highlight the challenges for practical deployments.
psi-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation

Sub-cortical brain structure segmentation is of great importance for diagnosing neuropsychiatric disorders. However, developing an automatic approach to segmenting sub-cortical brain structures remains very challenging due to the ambiguous boundaries, complex anatomical structures, and large variance of shapes. This paper presents a novel deep network architecture, namely Psi-Net, for sub-cortical brain structure segmentation, aiming at selectively aggregating features and boosting the information propagation in a deep convolutional neural network (CNN). To achieve this, we first formulate a densely convolutional LSTM module (DC-LSTM) to selectively aggregate the convolutional features with the same spatial resolution at the same stage of a CNN. This helps to promote the discriminativeness of features at each CNN stage. Second, we stack multiple DC-LSTMs from the deepest stage to the shallowest stage to progressively enrich low-level feature maps with high-level context. We employ two benchmark datasets on sub-cortical brain structure segmentation, and perform various experiments to evaluate the proposed Psi-Net. The experimental results show that our network performs favorably against the state-of-the-art methods on both benchmark datasets.
Reliability of Commercial UVC LEDs: 2022 State-of-the-Art

With this study, we report on the reliability of the most recent commercial UVC LED devices. The current COVID-19 pandemic urged the development of antiviral technologies, and one of the most effective is based on UVC irradiation, which can be effectively achieved by means of Deep UV LEDs. The development of antiviral systems based on UVC LEDs strongly depends on their efficacy and reliability. We propose an in-depth analysis of four different state-of-the-art commercial LEDs suitable for disinfection applications. LEDs have been subjected to a controlled stress test near their application limits, and their reliability and characteristics have been analyzed and studied. Results indicate a still limited reliability, with a degradation possibly related to an increase in Shockley-Read-Hall (SRH) recombination. Finally, some relevant product design suggestions will be proposed based on the results of this work.
Radiotherapy of non-small-cell lung cancer in the era of EGFR gene mutations and EGF receptor tyrosine kinase inhibitors

Non-small-cell lung cancer (NSCLC) occurs, approximately, in 80-85% of all cases of lung cancer. The majority of patients present locally advanced or metastatic disease when diagnosed, with poor prognosis. The discovery of activating mutations in the EGFR gene has started a new era of personalized treatment for NSCLC patients. To improve the treatment outcome in patients with unresectable NSCLC and, in particular, EGFR mutated, a combined strategy of radiotherapy and medical treatment can be undertaken. In this review we will discuss preclinical data regarding EGF receptor (EGFR) tyrosine kinase inhibitors (TKIs) and radiotherapy, available clinical trials investigating efficacy and toxicity of combined treatment (thoracic or whole brain radiotherapy and EGFR-TKIs) and, also, the role of local radiation in mutated EGFR patients who developed EGFR-TKI resistance.
Prediction of Harvested Energy for Wireless Sensor Node

Energy harvesting wireless sensor nodes are interesting for the Internet of Things, since they can provide continuous operation by adapting workload not only to the current energy reserves, but to the amount of energy that can be harvested in the future also. We present a multistage day ahead hourly solar energy prediction algorithm. The predictor uses cloud cover and precipitation probability predictions from weather forecast obtained once per day for 24 hours in advance. To compensate for short-term weather changes until the next weather forecast data is obtained, forecast errors of humidity and atmospheric pressure are fed to the fuzzy logic filter. The filter adjusts predictions of cloud cover and precipitation probability, which are applied to the clear-sky radiation model in order to obtain prediction of solar energy. The prediction of solar energy is additionally corrected based on the energy prediction error in the preceding time slot. The results show that the proposed predictor outperforms the state-of-the-art predictors in terms of prediction error. Proposed predictor and state-of-the-art predictors were also evaluated using a simulated wireless sensor node with the simple energy management algorithm, where the proposed predictor was the most efficient at maintaining energy neutrality.
Complexity measures and features for times series classification

Time series classification is a growing problem in different disciplines due to the progressive digitalization of the world. The best state-of-the-art algorithms focus on performance, seeking the best possible results, leaving interpretability at a second level, if any. Furthermore, interpretable proposals are far from providing competitive results. In this work, focused on time series classification, we propose a new representation of time series based on a robust and complete set of features. This new representation allows extracting more meaningful information on the underlying time series structure to develop effective classifiers whose results are much easier to interpret than current state-of-the-art models. The proposed feature set allows using the traditional vector-based classification algorithms in time series problems, significantly increasing the number of techniques available for this type of problem. To evaluate the performance of our proposal, we have used the state-of-the-art repository of time series classification, UCR, composed of 112 datasets. The experimental results show that through this representation, more interpretable classifiers can be obtained which are competitive. More specifically, they obtain no statistically significant differences from the second and third-best models of the state-of-the-art. Apart from competitive results in accuracy, our proposal is able to improve the model interpretability based on the set of features proposed.
LSB based non blind predictive edge adaptive image steganography

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image. Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.
Targeting inhibition of microtubule affinity regulating kinase 4 by Harmaline: Strategy to combat Alzheimer's disease

Microtubule-affinity regulating kinase 4 (MARK4) is linked with the development of cancer, diabetes and neurodegenerative diseases. Due to its direct role in the hyperphosphorylation of tau protein, MARK4 is considered as an attractive target to fight Alzheimer's disease (AD) and neuroinflammation. In the present study, we have selected Harmaline (HAR), an alkaloid of Paganum harmala, to investigate its MARK4 inhibitory potential and its binding mechanism. Molecular docking and fluorescence binding studies were carried out to estimate the binding affinity of the HAR with the MARK4. We observed an excellent binding affinity of HAR to the MARK4 (K = 107 M-1), further complemented by isothermal titration calorimetric measurements. In addition, HAR significantly inhibits the kinase activity of MARK4 (IC50 value of 4.46 μM). Structural investigations suggested that HAR binds to the active site pocket and forms several non-covalent interactions with biologically important residues of MARK4. All-atom molecular dynamics simulation studies further advocated that the MARK4-HAR complex is stabilized throughout the trajectory of 200 ns and causes a little conformational change. All these findings suggest that HAR is a potential MARK4 inhibitor that can be implicated in managing MARK4-associated diseases, including AD.
Speeding up the product release: a second-sphere contribution from Tyr191 to the reactivity of L-lactate oxidase revealed in crystallographic and kinetic studies of site-directed variants

Among α-hydroxy acid-oxidizing flavoenzymes l-lactate oxidase (LOX) is unique in featuring a second-sphere tyrosine (Tyr191 in Aerococcus viridans LOX; avLOX) at the binding site for the substrate's carboxylate group. Y191F, Y191L and Y191A variants of avLOX were constructed to affect a hydrogen-bond network connecting Tyr191 to the carboxylate of the bound ligand via the conserved Tyr40 and to examine consequent effects on enzymatic reactivity. Kinetic studies at 20 °C and pH 6.5 revealed that release of pyruvate product was decreased 4.7-fold (Y191F), 19-fold (Y191L) and 28-fold (Y191A) compared with wild-type enzyme (~ 141 s(-1)) and thus became mainly rate limiting for l-lactate oxidation by the variants at a steady-state under air-saturated conditions. In the Y191L and the Y191A variants, but not in the Y191F variant, l-lactate binding was also affected strongly by the site-directed substitution. Reduction of the flavin cofactor by l-lactate and its reoxidation by molecular oxygen were, however, comparatively weakly affected by the replacements of Tyr191. Unlike the related lactate monooxygenase, which prevents the fast dissociation of pyruvate to promote its oxidative decarboxylation by H2 O2 into acetate, CO2 and water as final reaction products, all avLOX variants retained their native oxidase activity where catalytic turnover results in the equivalent formation of H2O2. The 1.9 Å crystal structure of the Y191F variant bound with FMN and pyruvate revealed a strictly locally disruptive effect of the site-directed substitution. Product off-rates appear to be dictated by partitioning of residues including Tyr191 from an active-site lid loop into bulk solvent and modulation of the hydrogen bond strength that links Tyr40 with the pyruvate's carboxylate group. Overall, this study emphasizes the possibly high importance of contributions from second-sphere substrate-binding residues to the fine-tuning of reactivity in α-hydroxy acid-oxidizing flavoenzymes, requiring that the catalytic steps of flavin reduction and oxidation are properly timed with the physical step of α-keto acid product release.
Towards Low-Power High-Efficiency RF and Microwave Energy Harvesting

Since the very beginning of RF and microwave integrated techniques and energy harvesting, Schottky diodes are most often used in mixing and rectifying circuits. However, in specific W power-harvesting applications, the Schottky diode technique fails to provide a satisfactory RF-dc conversion efficiency mainly because of its high zero-bias junction resistance. This paper examines the state-of-the-art low-power microwave-to-dc energy conversion techniques. A comprehensive picture of the state-of-the-art on this aspect is given graphically, which compares different technologies such as transistor, diode, and CMOS schemes. Subsequent to the highlighted limitations of current devices, this work introduces, for the first time, a nonlinear component for low-power rectification based on a recent discovery in spintronics, namely, the spindiode. Along with an analysis of the role of nonlinearity and zero bias resistance in the rectification process of the spindiode, it is shown how the spindiode could enhance the rectification efficiency even at a very low-power level and how this technique would shift the design paradigms of diode-based devices and circuits.