ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Contrasting effects of chloride on growth, reproduction, and toxicant sensitivity in two genetically distinct strains of Hyalella azteca

The strain of Hyalella azteca (Saussure: Amphipoda) commonly used for aquatic toxicity testing in the United States has been shown to perform poorly in some standardized reconstituted waters frequently used for other test species. In 10-d and 42-d experiments, the growth and reproduction of the US laboratory strain of H. azteca was shown to vary strongly with chloride concentration in the test water, with declining performance observed below 15 mg/L to 20 mg/L. In contrast to the chloride-dependent performance of the US laboratory strain of H. azteca, growth of a genetically distinct strain of H. azteca obtained from an Environment Canada laboratory in Burlington, Ontario, Canada, was not influenced by chloride concentration. In acute toxicity tests with the US laboratory strain of H. azteca, the acute toxicity of sodium nitrate increased with decreasing chloride in a pattern similar not only to that observed for control growth, but also to previous acute toxicity testing with sodium sulfate. Subsequent testing with the Burlington strain showed no significant relationship between chloride concentration and the acute toxicity of sodium nitrate or sodium sulfate. These findings suggest that the chloride-dependent toxicity shown for the US laboratory strain may be an unusual feature of that strain and perhaps not broadly representative of aquatic organisms as a whole.
Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to: 1) large anatomical and spatial variations of splenomegaly; 2) large inter- and intra-scan intensity variations on multi-modal MRI; and 3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3-D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method has achieved the highest median and mean Dice scores among the investigated baseline DCNN methods.
Non-Intrusive Load Monitoring via Multi-Label Sparse Representation-Based Classification

This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular sparse representation based classification (SRC) approach (developed for single label classification) to solve multi-label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state-of-the-art techniques with small volume of training data.
Closed-Loop Attention Restoration Theory for Virtual Reality-Based Attentional Engagement Enhancement

Today, as media and technology multitasking becomes pervasive, the majority of young people face a challenge regarding their attentional engagement (that is, how well their attention can be maintained). While various approaches to improve attentional engagement exist, it is difficult to produce an effect in younger people, due to the inadequate attraction of these approaches themselves. Here, we show that a single 30-min engagement with an attention restoration theory (ART)-inspired closed-loop software program (Virtual ART) delivered on a consumer-friendly virtual reality head-mounted display (VR-HMD) could lead to improvements in both general attention level and the depth of engagement in young university students. These improvements were associated with positive changes in both behavioral (response time and response time variability) and key electroencephalography (EEG)-based neural metrics (frontal midline theta inter-trial coherence and parietal event-related potential P3b). All the results were based on the comparison of the standard Virtual ART tasks (control group, n = 15) and closed-loop Virtual ART tasks (treatment group, n = 15). This study provides the first case of EEG evidence of a VR-HMD-based closed-loop ART intervention generating enhanced attentional engagement.
2'-Fucosyllactose Promotes the Production of Short-Chain Fatty Acids and Improves Immune Function in Human-Microbiota-Associated Mice by Regulating Gut Microbiota

As a natural prebiotic in human milk, 2'-fucosyllactose (2'-FL) is actively used in infant formula (IF). However, the 2'-FL influence on the improvement of gut microbiota and the regulation of the immune function remains unknown. In this study, human microbiota-associated (HMA) mice were used to demonstrate that feeding 2'-FL-containing IF was comparable to human milk at levels of immune cytokines (IL-2, IL-9, IL-10, and sIgA) and short-chain fatty acids (SCFAs, i.e., acetate and propionate). In addition, 2'-FL increased the abundance of Blautia and Olsenella and improved the anti-inflammatory cytokine IL-10 levels. The abundance of Blautia and Olsenella positively correlated with the IL-10 levels. 2'-FL also decreased the abundance of Enterorhabdus and Lachnospiraceae_UCG-006 and elevated SCFA levels, showing a negative correlation between these genera and SCFAs. Our findings revealed that feeding 2'-FL-containing IF drives the levels of cytokines and SCFAs toward human milk levels by shaping the beneficial gut microbiota profile.
Dense semantic embedding network for image captioning

Recently, attributes that contain high-level semantic information of image are always used as a complementary knowledge to improve image captioning performance. However, the use of attributes in prior works cannot excavate the latent visual concepts effectively. At each time step, the semantic information which is sensitive to the predicted word could be different. In this paper, we propose a Dense Semantic Embedding Network (DSEN) for this task. The distinct operation of this network is to densely embed the attributes with the multi-modal of image and text at each step of word generation. The discriminative semantic information hidden in these attributes is formatted in form of global likelihood probabilities. As a result, this dense embedding can modulate the feature distributions of the image, text modals and the hidden states to explicit semantic representation. Furthermore, to improve the discrimination of attributes, a Threshold ReLU (TReLU) is proposed. In addition, a bidirectional LSTM structure is incorporated into the DSEN to capture both the previous and future contexts. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art models for the tasks of both image captioning and image-text cross modal retrieval. Most remarkably, our method obtains outstanding performance on the retrieval task, compared with the state-of-the-art models. (C) 2019 Published by Elsevier Ltd.
Evaluation of Antitumor and On-Target Activity of HDAC Inhibitors with the Zebrafish Embryo Xenograft Model

Reliable preclinical drug testing models for cancer research are urgently needed with zebrafish embryo models emerging as a powerful vertebrate model for xenotransplantation studies. Here, we describe the evaluation of toxicity, efficacy, and on-target activity of histone deacetylase (HDAC) inhibitors in a zebrafish embryo yolk sac xenotransplantation model of medulloblastoma and neuroblastoma cells. For this, we performed toxicity assays with our zebrafish drug library consisting of 28 clinically relevant targeted as well as chemotherapeutic drugs with zebrafish embryos. We further engrafted zebrafish embryos with fluorescently labeled pediatric tumor cells (SK-N-BE(2)-C, HD-MB03, or MED8A) and monitored the progression after HDAC inhibitor treatment of xenotransplanted tumors through tumor volume measurements with high-content confocal microscopy in a multi-well format. The on-target activity of HDAC inhibitors was verified through immunohistochemistry staining on paraffin-embedded early larvae. Overall, the zebrafish embryo xenotransplantation model allows for fast and cost-efficient in vivo evaluation of targeted drug toxicity, efficacy, and on-target activity in the field of precision oncology.
SRSF5-Mediated Alternative Splicing of M Gene is Essential for Influenza A Virus Replication: A Host-Directed Target Against Influenza Virus

Splicing of influenza A virus (IAV) RNA is an essential process in the viral life cycle that involves the co-opting of host factors. Here, it is demonstrated that induction of host serine and arginine-rich splicing factor 5 (SRSF5) by IAV facilitated viral replication by enhancing viral M mRNA splicing. Mechanistically, SRSF5 with its RRM2 domain directly bounds M mRNA at conserved sites (M mRNA position 163, 709, and 712), and interacts with U1 small nuclear ribonucleoprotein (snRNP) to promote M mRNA splicing and M2 production. Mutations introduced to the three binding sites, without changing amino acid code, significantly attenuates virus replication and pathogenesis in vivo. Likewise, SRSF5 conditional knockout in the lung protects mice against lethal IAV challenge. Furthermore, anidulafungin, an approved antifungal drug, is identified as an inhibitor of SRSF5 that effectively blocks IAV replication in vitro and in vivo. In conclusion, SRSF5 as an activator of M mRNA splicing promotes IAV replication and is a host-derived antiviral target.
GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs

In the last two years learning-based methods have started to show encouraging results in different supervised and unsupervised medical image registration tasks. Deep neural networks enable (near) real time applications through fast inference times and have tremendous potential for increased registration accuracies by task-specific learning. However, estimation of large 3D deformations, for example present in inhale to exhale lung CT or interpatient abdominal MRI registration, is still a major challenge for the widely adopted U-Net-like network architectures. Even when using multi-level strategies, current state-of-the-art DL registration results do not yet reach the high accuracy of conventional frameworks. To overcome the problem of large deformations for deep learning approaches, in this work, we present GraphRegNet, a sparse keypoint-based geometric network for dense deformable medical image registration. Similar to the successful 2D optical flow estimation of FlowNet or PWC-Net we leverage discrete dense displacement maps to facilitate the registration process. In order to cope with enormously increasing memory requirements when working with displacement maps in 3D medical volumes and to obtain a well-regularised and accurate deformation field we 1) formulate the registration task as the prediction of displacement vectors on a sparse irregular grid of distinctive keypoints and 2) introduce our efficient GraphRegNet for displacement regularisation, a combination of convolutional and graph neural network layers in a unified architecture. In our experiments on exhale to inhale lung CT registration we demonstrate substantial improvements (TRE below 1.4 mm) over other deep learning methods. Our code is publicly available at https://github.com/multimodallearning/graphregnet.
STUDY ON TRANSFORMATION OF INDOLE BY LACCASE IN THE PRESENCE OF PHENOL AND 2,2-AZINO-BIS (3-ETHYLBENZTHIAZOLINE-6-SULPHONIC ACID)

Transformation of indole by laccase in the presence of phenol using 2, 2-Azino-bis (3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) as mediator was studied. The experiments were performed with different pH, enzyme dosage, ARTS and phenol concentration. The results showed that the degradation efficiency of indole was better in the acid solution. ARTS and phenol had significant effect on transformation of indole in the liquid substrate. Phenol could be oxidized by laccase substrate, and also enhance the degradation of indole. The intermediates of phenol could act as laccase mediator different from ABTS, which enhanced transformation of indole.
Practical Measurement of Changes in Leg Length Discrepancy After a Myofascial Release on the Thoracolumbar Fascia in Patients With Acute Low Back Pain: A Pilot Study

Background Recent work has examined an association between leg length discrepancy (LLD) and low back pain (LBP). Myofascial release (MFR) techniques are thought to be frequently applied in the treatment of chronic and acute LBP. The purpose of this study was to evaluate a practical measure of LLD and the feasibility of an MFR technique in a randomised controlled trial (RCT). Methods In 12 subjects (seven women and five men) with acute LBP and LLD greater than 3 mm, an MFR technique was performed on the thoracolumbar fascia. At the baseline, after the intervention, and at follow-up, LLD was measured using a cross-line laser and finger-to-floor distance, and the pain was measured with a visual analogue scale (VAS). Patients completed a questionnaire after follow-up to assess the acceptability of the study procedure. The therapist evaluated the methods in terms of their feasibility. Results LLD measurement and MFR treatment required little time and few resources. Participants agreed to the study procedure with moderate to high acceptance. The LLD decreased by 5 mm after treatment and by 4 mm at follow-up. The VAS showed a reduction in pain of 17.50 mm at follow-up but not immediately after treatment. Conclusion The measurement of LLD is applicable in daily osteopathic practice, but it cannot be assumed to be a valid method for an RCT. Validated methods such as video raster stereography are, therefore, recommended. Comprehensive RCTs to study the effects of MFR intervention on leg length are feasible.
MFNW: An MLC/TLC Flip-N-Write Architecture

The increased capacity of multi-level cells (MLC) and triple-level cells (TLC) in emerging non-volatile memory (NVM) technologies comes at the cost of higher cell write energies and lower cell endurance. In this article, we describe MFNW, a Flip-N-Write encoding that effectively reduces the write energy and improves the endurance of MLC NVMs. Two MFNW modes are analyzed: cell Hamming distance mode and energy Hamming distance mode. We derive an approximate model that accurately predicts the average number of cell writes that is proportional to the energy consumption, enabling word length optimization to maximize energy reduction subject to memory space overhead constraints. In comparison to state-of-the-art MLC NVM encodings, our simulation results indicate that MFNW achieves up to 7%-39% saving for 1.56%-50% NVM space overhead. Extra energy saving (up to 19%-47%) can be achieved for the same NVM space overhead using our proposed variations of MFNW, i.e., MFNW2 and MFNW3. For TLC NVMs, we propose TFNW that can achieve up to 53% energy saving in comparison to state-of-the-art TLC NVM encodings. Endurance simulations indicate that MFNW (TFNW) is capable of extending MLC (TLC) NVM life by up to 100% (87%).
EEG decoding method based on multi-feature information fusion for spinal cord injury

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.
HARD-PnP: PnP Optimization Using a Hybrid Approximate Representation

This paper proposes a Hybrid Approximate Representation (HAR) based on unifying several efficient approximations of the generalized reprojection error (which is known as the gold standard for multiview geometry). The HAR is an over-parameterization scheme where the approximation is applied simultaneously in multiple parameter spaces. A joint minimization scheme "HAR-Descent" can then solve the PnP problem efficiently, while remaining robust to approximation errors and local minima. The technique is evaluated extensively, including numerous synthetic benchmark protocols and the real-world data evaluations used in previous works. The proposed technique was found to have runtime complexity comparable to the fastest Oonthorn techniques, and up to 10 times faster than current state of the art minimization approaches. In addition, the accuracy exceeds that of all 9 previous techniques tested, providing definitive state of the art performance on the benchmarks, across all 90 of the experiments in the paper and supplementary material, which can be found on the Computer Society Digital Library at http:w//doi.ieeecomputersociety.org/10.1109/TPAMI.2018.2806446.
Frequency of Nucleophosmin 1 Expression by Immunohistochemistry in Acute Myeloid Leukemia

Nucleophosmin (NPM1) mutation is one of the most common recurring genetic abnormalities seen in acute myeloid leukemia (AML). Immunohistochemistry serves as a cost effective and simple surrogate testing method for detection of NPM1 mutation. This study was conducted to evaluate the frequency of aberrant cytoplasmic nucleophosmin 1 expression in leukemic blast cells on formalin fixed bone marrow trephine biopsy (BMB) sections and also to correlate this data with the reference molecular method (reverse transcriptase-polymerase chain reaction; RT-PCR and gene sequencing), where available. Immunostains were performed using mouse anti-NPM1 monoclonal antibody on 71 paraffin embedded bone marrow biopsies (BMB) of patients with AML of any French-American-British (FAB) subtype. Results of immunohistochemistry (IHC) were then compared with the reference molecular method. The proportion of NPM1 expression by immunostaining in AML cases was found to be 17%. Twelve of the total 71 cases demonstrated cytoplasmic nucleophosmin (NPMc+) on immunostaining. Eleven of the positive cases that were correlated with the molecular standard demonstrated mutation in exon 12 of NPM1 gene. Cytoplasmic nucleophosmin expression by immunostaining was found to be in complete agreement with the standard molecular method. In a resource restricted setup, the information from this study might help in providing an inexpensive and accurate detection method to facilitate introduction of this marker in diagnostic and prognostic workup of AML especially in patients showing normal karyotype and no common recurrent translocations.
Pd Nanoparticle-Loaded Smart Microgel-Based Membranes as Reusable Catalysts

In this work, palladium-loaded smart membranes made by UV cross-linking of thermoresponsive microgels are prepared to obtain a reusable, catalytically active material which can, for example, be implemented in chemical reactors. The membranes are examined with respect to their coverage of a supporting mesh via atomic force microscopy measurements. Force indentation mapping was performed in the dried, collapsed state and in the swollen state in water to determine the Young modulus. Furthermore, we compare the catalytic activity of the membrane with the corresponding suspended colloidal nanoparticle microgel hybrids. For this purpose, the reduction of 4-nitrophenol is an established model reaction to quantify the catalytic activity by UV-vis spectroscopy. The membrane is embedded inside a continuous stirred tank reactor equipped for continuous monitoring of the reaction progress. Although catalysis with membranes shows lower catalytic activity than freely dispersed particles, membranes allow straightforward separation and recycling of the catalyst. The fabricated membranes in this work show no decrease in catalytic activity between several cycles, unlike free particles. The feasible and durable deposition of catalytically active inter-cross-linked microgel particles on commercial nylon meshes as supporting scaffolds, as demonstrated in this work, is promising for up-scaling of continuous industrial processes.
Feasibility, Acceptability, and Preliminary Impact of Full-Body Interaction on Computerized Cognitive Training Based on Instrumental Activities of Daily Living: A Pilot Randomized Controlled Trial with Chronic Psychiatric Inpatients

Objective: To conduct a pilot randomized control trial to assess the feasibility and acceptability of full-body interaction cognitive training (FBI-CT) inspired by instrumental activities of daily living in chronic psychiatric inpatients and to explore its preliminary impact on cognitive and noncognitive outcomes. Materials and Methods: Twenty psychiatric inpatients met the inclusion criteria and were randomly allocated to the FBI-CT group (n = 10) or the tablet-based CT group (T-CT) (n = 10). Neuropsychological assessments were performed at baseline, postintervention, and 3-month follow-up. Results: Both groups presented high completion rates at postintervention and follow-up. Participants reported high satisfaction following the interventions, with the FBI-CT group exhibiting slightly higher satisfaction. A within-group analysis showed significant improvements in the FBI-CT group for processing speed and sustained attention for short periods (P = 0.012), verbal memory (P = 0.008), semantic fluency (P = 0.027), depressive symptoms (P = 0.008), and quality of life (P = 0.008) at postintervention. At 3-month follow-up, this group maintained verbal memory improvements (P = 0.047) and depressive symptoms amelioration (P = 0.026). The T-CT group revealed significant improvements in sustained attention for long periods (P = 0.020), verbal memory (P = 0.014), and executive functions (P = 0.047) postintervention. A between-group analysis demonstrated that the FBI-CT group exhibited greater improvements in depressive symptoms (P = 0.042). Conclusions: Overall, we found support for the feasibility and acceptability of both training approaches. Our findings show promise regarding the preliminary impact of the FBI-CT intervention, but due to study limitations such as the small sample size, we cannot conclude that FBI-CT is a more effective approach than T-CT for enhancing cognitive and noncognitive outcomes of chronic psychiatric inpatients. Clinical trials (number: NCT05100849).