Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Validation of a Numerical Model for the Mechanical Behavior of Knee Prosthesis Analyzed by the Finite Elements Method

The human knee is a complex joint (the largest joint of the human body). During the different daily activities, this joint is exposed to significant loads and movements, may in some cases exceed the limit of the mechanical capacities of its components, which shows that the pathologies are quite numerous at the level of the human knee and the treatment sometimes requires surgery to either repair or implant (implant total knee prosthesis). As we know very well, the success of a total knee implant is highly dependent on the initial stability of the femoral or tibial implant and the integration of femur and tibia bone tissue with these implants in the long term. Due to the optimal distribution of mechanical stresses in the surrounding bone. It is for this reason that the search for reasonable solutions to compensate the damaged knee prosthesis and reduce the stresses in the cortical bone and spongy has become a very important research axis. In this regard, we have proposed three models of prosthesis knee joint from available literature and study the distribution of Von-Mises stresses and strains in the differents composents of knee prosthesis, know the total displacement between the model intact and model artificial of knee, 3D modeling software Solidworks 2016 is used for 3D modeling of knee prosthesis and finite element analysis software ANSYS 16.2 is used for numerical estimation of von-Mises stresses and strains. We find in this study that the maximum stresses and strains of Von Mises at the level of the tibia and tibial bone decrease, that is to say that the cement and the elastomer play a very important role in the absorption of the stresses and their minimization. On the other hand, the four knee prostheses (Model I (Ti6Al4V), Model II (CoCrMo), Model III (316L SS), Model IV (ZrO2)) implanted by elastomer contribute significantly to the reduction of stresses in the patella bone compared to the Intact Model. In general, both models of the knee prosthesis and reinforced by a stress reduction system (cement, elastomer) gave a lower stress level in the tibia and tibial bone of a normal person compared to a healthy model. The results obtained provide a theoretical basis for choosing an appropriate surgical model.
Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing

We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.
AdaError: An Adaptive Learning Rate Method for Matrix Approximation-based Collaborative Filtering

Gradient-based learning methods such as stochastic gradient descent are widely used in matrix approximation-based collaborative filtering algorithms to train recommendation models based on observed user-item ratings. One major difficulty in existing gradient-based learning methods is determining proper learning rates, since model convergence would be inaccurate or very slow if the learning rate is too large or too small, respectively. This paper proposes AdaError, an adaptive learning rate method for matrix approximation-based collaborative filtering. AdaError eliminates the need of manually tuning the learning rates by adaptively adjusting the learning rates based on the noisiness level of user-item ratings, using smaller learning rates for noisy ratings so as to reduce their impact on the learned models. Our theoretical and empirical analysis shows that AdaError can improve the generalization performance of the learned models. Experimental studies on the MovieLens and Netflix datasets also demonstrate that AdaError outperforms state-of-the-art adaptive learning rate methods in matrix approximation-based collaborative filtering. Furthermore, by applying AdaError to the standard matrix approximation method, we can achieve statistically significant improvements over state-of-the-art collaborative filtering methods in both rating prediction accuracy and top-N recommendation accuracy.
Qualitative analysis of parents’ experience with early detection of hearing loss

Aims: To determine key themes from parents’ comments on paths to diagnosis and intervention for their children with hearing loss, following introduction of at-risk neonatal hearing screening and modification of distraction test screening for infants not at-risk. Methods: Parents of children born in 1993 in Victoria, Australia, who were eligible for screening via the Victorian Infant Hearing Screening Program and who were subsequently diagnosed with a permanent congenital hearing loss and fitted with hearing aids prior to the year 2000 were asked to complete a semi-structured questionnaire shortly after aid fitting. Two researchers independently analysed parent comments using the constant comparative method. Results: Parents of 82 children (61%) replied to the questionnaire. Themes analysis revealed a generally positive response to neonatal ABR screening, with a mixed response to the distraction test; powerful emotions experienced by parents at diagnosis including denial and shock; frustration arising from delays in diagnosis, and communication difficulties with providers. Special difficulties testing children with other medical and developmental problems, confusion about tympanostomy tube insertion, and difficulty with wearing hearing aids were also reported. Some children had experienced problems in the school setting. Experience of post-diagnostic services was generally positive. Conclusions: Parents need greater support both during the testing of screen failures and at the time of diagnosis. Providers need more training in how to communicate findings to parents, particularly at times when parents are experiencing strong emotions. Parents need more strategies to enable hearing aid wearing in very young children. Some children with additional medical, developmental, and behavioural problems need specialised approaches to testing.
An international study on the feasibility of a standardized combined plasma clot turbidity and lysis assay: communication from the SSC of the ISTH

M. P IETERS ,* H . PH IL I PPOU,† A. UNDAS ,‡ Z . DE LANGE ,* D . C . R I JKEN§ and N. J . MUTCH ¶ FOR THE SUBCOMMITTEE ON FACTOR X I I I AND F IBR INOGEN, AND THE SUBCOMMITTEE ON F IBR INOLYS I S *Centre of Excellence for Nutrition, North-West University, Potchefstroom, South Africa; †Division of Cardiovascular and Diabetes Research, LICAMM, University of Leeds, Leeds, UK; ‡Institute of Cardiology, Jagiellonian University School of Medicine, and John Paul II Hospital, Krakow, Poland; §Department of Hematology, Erasmus University Medical Center, Rotterdam, the Netherlands; and ¶School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
Characteristics of the Nial/Ni3al Matrix Composite With Tib2 Particles Fabricated By High Pressure – High Temperature Sintering

The article presents the results of tests carried out on the manufactured composite materials based on a two-phase NiAl/Ni 3 Al matrix, which was enriched with the addition of TiB 2 ceramic particles added in an amount of 4 and 7 vol%. The resulting mixtures were sintered by the High Pressure High Temperature (HP-HT) process. The results were compared to the results obtained for the sole matrix material produced under the same conditions. It has been shown that, at a lower density, the addition of reinforcing particles increases the composite hardness, Young’s modulus and resistance to frictional wear. However, higher addition of TiB 2 (7 vol%) was observed to yield less satisfactory results, and despite higher hardness and lower density caused a decrease in other properties tested. The produced materials were characterized by a compact and highly differentiated microstructure free from any noticeable cracks and pores.
Static face detection and emotion recognition with FPGA support

Throughout history, spoken language and face-to-face communication have been the primary mechanics of interaction between two or more people. While processing speech, it is often advantageous to determine the emotion of the speaker in order to better understand the context of the meaning. This paper looks at our current effort at creating a static based emotion detection system, using previously used techniques along with a custom FPGA neural network to speed up
Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews

Collaborative filtering (CF) is a common recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. Moreover, in some CF approaches latent features are often used to represent users and items, which can lead to a lack of recommendation transparency and explainability. User-generated, customer reviews are now commonplace on many websites, providing users with an opportunity to convey their experiences and opinions of products and services. As such, these reviews have the potential to serve as a useful source of recommendation data, through capturing valuable sentiment information about particular product features. In this paper, we present a novel deep learning recommendation model, which co-learns user and item information from ratings and customer reviews, by optimizing matrix factorization and an attention-based GRU network. Using real-world datasets we show a significant improvement in recommendation performance, compared to a variety of alternatives. Furthermore, the approach is useful when it comes to assigning intuitive meanings to latent features to improve the transparency and explainability of recommender systems.
Modeling signaling pathways in articular cartilage

Chondrocytes, the only cell type in articular cartilage, are responsible for maintaining the composition of cartilage extracellular matrix (ECM) through a complex interplay of anabolic and catabolic stimuli. Although understanding the way chondrocytes respond to stimuli is of utmost importance for shedding light into the etiology of joint diseases, an integrative approach to studying their signaling transduction mechanisms is yet to be introduced. Herein, we propose an approach that combines high throughput proteomic measurements and state of the art optimization algorithms to construct a predictive model of chondrocyte signaling network, downstream of 78 receptors of interest.
Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Given a time-evolving graph, how can we track similarity between nodes in a fast and accurate way, with theoretical guarantees on the convergence and the error? Random Walk with Restart (RWR) is a popular measure to estimate the similarity between nodes and has been exploited in numerous applications. Many real-world graphs are dynamic with frequent insertion/deletion of edges; thus, tracking RWR scores on dynamic graphs in an efficient way has aroused much interest among data mining researchers. Recently, dynamic RWR models based on the propagation of scores across a given graph have been proposed, and have succeeded in outperforming previous other approaches to compute RWR dynamically. However, those models fail to guarantee exactness and convergence time for updating RWR in a generalized form. In this paper, we propose OSP, a fast and accurate algorithm for computing dynamic RWR with insertion/deletion of nodes/edges in a directed/undirected graph. When the graph is updated, OSP first calculates offset scores around the modified edges, propagates the offset scores across the updated graph, and then merges them with the current RWR scores to get updated RWR scores. We prove the exactness of OSP and introduce OSP-T, a version of OSP which regulates a trade-off between accuracy and computation time by using error tolerance? Given restart probability c, OSP-T guarantees to return RWR scores with O(∋/c) error in O(log(∋/2)/log(1-c)) iterations. Through extensive experiments, we show that OSP tracks RWR exactly up to 4605x faster than existing static RWR method on dynamic graphs, and OSP-T requires up to 15x less time with 730x lower L1 norm error and 3.3x lower rank error than other state-of-the-art dynamic RWR methods.
Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks

One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.
Nature, prevalence and factors associated with depression among the elderly in a rural south Indian community

ABSTRACT Background: Depression in old age is an important public health problem causing considerable morbidity and disability worldwide. There is a dearth of community studies from India investigating geriatric depression and its associated risk factors. This study aimed to establish the nature, prevalence and factors associated with geriatric depression in a rural south Indian community. Methods: We recruited 1000 participants aged over 65 years from Kaniyambadi block, Vellore, India. We assessed their socio-demographic profile, psychiatric morbidity, cognitive functioning, anthropometrics and disability status using the following structured assessment tools: Geriatric Mental State, Community Screening Instrument for Dementia, Modified CERAD 10 word list learning task, History and Aetiology Schedule Dementia Diagnosis and Subtype, WHO Disability Assessment Scale II, and Neuropsychiatric Inventory. We adopted a case control framework to study the factors associated with geriatric depression. Results: Prevalence of geriatric depression (ICD-10) within the previous one month was 12.7% (95% CI 10.64–14.76%). Low income (OR 1.78; 95% CI 1.08–2.91), experiencing hunger (OR 2.58; 95% CI 1.56–4.26), history of cardiac illnesses (OR 4.75; 95% CI 1.96–11.52), transient ischemic attack (OR 2.43; 95% CI 1.17–5.05), past head injury (OR 2.70; 95% CI 1.36–5.36) and diabetes (OR 2.33; 95% CI 1.15–4.72) increased the risk for geriatric depression after adjusting for other determinants using conditional logistic regression. Having more confidants (OR 0.13; 95% CI 0.06–0.26) was the significant protective factor. Age, female gender, cognitive impairment and disability status were not significantly associated with geriatric depression. DSM-IV diagnosis of major depression was significantly correlated with experiencing hunger, diabetes, transient ischemic attack, past head injury, more disability and less nourishment; having more friends was protective. Conclusions: Geriatric depression is prevalent in rural south India. Poverty and physical ill health are risk factors for depression among elderly while good social support is protective.
Experiences and psychological distress of fertility treatment and employment

Abstract Purpose: This study examined experiences and psychological distress about fertility treatment in people combining work and treatment. Methods: Five hundred and sixty-three participants in the UK completed an online survey asking about difficulties in combining work and treatment; workplace disclosure, support, absence and policy; and psychological distress about treatment. Results: Absence from work and perceptions that treatment has an impact on work and career prospects were reported by the majority of participants and this was related to the psychological distress of treatment. Around three quarters of participants disclosed to their employer and colleagues. The key reason for disclosure was needing to ask for absence from work and the main reason for non-disclosure was privacy. Workplace policy relating to managing fertility treatment and support from colleagues and their employer was related to reduced psychological distress but workplace policy was reported by less than one quarter of participants. Conclusion: Difficulties experienced in combining work and treatment suggest that support is needed. Specific workplace policy, guidance for supervisors and flexibility in fertility clinic times should help support employees during treatment and reduce psychological distress, thereby potentially influencing physical health and treatment outcomes.
Chemiluminescence response induced by mesenteric ischaemia/reperfusion: effect of antioxidative compounds ex vivo

Chemiluminescence response induced by mesenteric ischaemia/reperfusion: effect of antioxidative compounds ex vivo Ischaemia and reperfusion (I/R) play an important role in human pathophysiology as they occur in many clinical conditions and are associated with high morbidity and mortality. Interruption of blood supply rapidly damages metabolically active tissues. Restoration of blood flow after a period of ischaemia may further worsen cell injury due to an increased formation of free radicals. The aim of our work was to assess macroscopically the extent of intestinal pathological changes caused by mesenteric I/R, and to study free radical production by luminol enhanced chemiluminescence (CL) of ileal samples. In further experiments, the antioxidative activity of the drugs tested was evaluated spectrophotometrically by the use of the DPPH radical. We studied the potential protective ex vivo effect of the plant origin compound arbutin as well as of the pyridoindole stobadine and its derivative SMe1EC2. I/R induced pronounced haemorrhagic intestinal injury accompanied by increase of myeloperoxidase (MPO) and N-acetyl-β-D-glucosaminidase (NAGA) activity. Compared to sham operated (control) rats, there was only a slight increase of CL response after I/R, probably in association with neutrophil increase, indicated by enhanced MPO activity. All compounds significantly reduced the peak values of CL responses of the ileal samples ex vivo, thus reducing the I/R induced increase of free radical production. The antioxidants studied showed a similar inhibitory effect on the CL response influenced by mesenteric I/R. If proved in vivo, these compounds would represent potentially useful therapeutic antioxidants.
An Isolated Data Encryption Experiment Method by Utilizing Baseband Processors

With the rapid development of Android systems and the growing of Android market, Android system has become a focus of developers and users. MTK6795 is System-on-a-chip (SoC), which is specially designed by MediaTek for high-end smart phones. It integrates the application processor and the baseband processor in just one chip. In this paper, a new encryption method based on the baseband processor of MT6795 SoC is proposed and successfully applied on one Android-based smart phone to protect user data. In this method, the encryption algorithm and private user data are isolated into two processors, which improves the security of users' private data.
Cell penetrating peptides: a comparative transport analysis for 474 sequence motifs

Abstract Delivering reagents into cells is a key demand in molecular medicine. The vehicle of choice is often cell penetrating peptides (CPPs), which can ferry conjugated cargo across membranes. Although numerous peptides have been shown to promote such uptake events, there has been no comprehensive comparison of individual performance under standardized conditions. We have devised a method to rapidly analyze the ability of a multitude of CPP conjugates to carry a model cargo into HeLa cells. Sequence information for 474 CPPs was collected from literature sources, and the respective peptides were synthesized and modified with carboxyfluorescein (FAM) as model cargo. All candidates were evaluated in an identical uptake test, and transport was quantified using cellular fluorescence intensities. Substantial differences in the ability to carry the fluorophore into the cells were observed, with transport performance differing by a factor of 70 between the best CPP investigated and cargo alone. Strong correlations were observed between uptake efficiency and both sequence length and the presence of positive net charge. A compilation of the 20 top performers with regard to cargo delivery performance and cell compatibility is provided.
Effects of dietary plant meal and soya-saponin supplementation on intestinal and hepatic lipid droplet accumulation and lipoprotein and sterol metabolism in Atlantic salmon (Salmo salar L.)

Altered lipid metabolism has been shown in fish fed plant protein sources. The present study aimed to gain further insights into how intestinal and hepatic lipid absorption and metabolism are modulated by plant meal (PM) and soya-saponin (SA) inclusion in salmon feed. Post-smolt Atlantic salmon were fed for 10 weeks one of four diets based on fishmeal or PM, with or without 10 g/kg SA. PM inclusion resulted in decreased growth performance, excessive lipid droplet accumulation in the pyloric caeca and liver, and reduced plasma cholesterol levels. Intestinal and hepatic gene expression profiling revealed an up-regulation of the expression of genes involved in lipid absorption and lipoprotein (LP) synthesis (apo, fatty acid transporters, microsomal TAG transfer protein, acyl-CoA cholesterol acyltransferase, choline kinase and choline-phosphate cytidylyltransferase A), cholesterol synthesis (3-hydroxy-3-methylglutaryl-CoA reductase) and associated transcription factors (sterol regulatory element-binding protein 2 and PPARγ). SA inclusion resulted in reduced body pools of cholesterol and bile salts. The hepatic gene expression of the rate-limiting enzyme in bile acid biosynthesis (cytochrome P450 7A1 (cyp7a1)) as well as the transcription factor liver X receptor and the bile acid transporter abcb11 (ATP-binding cassette B11) was down-regulated by SA inclusion. A significant interaction was observed between PM inclusion and SA inclusion for plasma cholesterol levels. In conclusion, gene expression profiling suggested that the capacity for LP assembly and cholesterol synthesis was up-regulated by PM exposure, probably as a compensatory mechanism for excessive lipid droplet accumulation and reduced plasma cholesterol levels. SA inclusion had hypocholesterolaemic effects on Atlantic salmon, accompanied by decreased bile salt metabolism.