Miracle-dz/UniFAR-FTUs huggingface dataset:
Antisense oligonucleotide targeting TNF‐α can suppress co‐cr‐mo particle‐induced osteolysis

The most common cause of implant failure in joint replacement is aseptic loosening due to particle‐induced osteolysis. TNF‐α has been shown to be one of the key factors in the process of osteoclastogenesis. Anti‐TNF agents are useful in the treatment of joint inflammation related to osteolysis. This study investigated the effect of a single subcutaneous dose of an antisense oligonucleotide (ASO) on particle‐induced osteolysis. We utilized the murine calvaria osteolysis model in C57BL/J6 mice. Bone resorption was measured by the toluidine blue staining. Osteoclasts were detected by tartrate resistant acid phosphatase (TRAP) staining assay and were quantified by a TRAP quantification kit. Results show that bone resorption is 0.347 ± 0.09 mm2 in mice with particle implantation, and decreased to 0.123 ± 0.05 mm2 and 0.052 ± 0.02 mm2 after ASO treatment with low and high doses, respectively. The number of osteoclasts in animal calvaria treated with ASO is reduced compared with that of untreated animals, and the quantification results indicate that about 90% of osteoclastogenesis is suppressed by the ASO. In addition, the osteoclastogenesis can be reestablished by the addition of TNF‐α. In conclusion, we demonstrate that the antisense oligonucleotide targeting to TNF‐α can suppress osteolysis induced by metal particles in a murine calvaria model. This new finding may be of value in the search for novel therapeutic methods for implant loosening. © 2008 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 26:1114–1120, 2008
Stable and improved generative adversarial nets (GANS): A constructive survey

In this paper, we present a general and applicable adversarial training framework based on a comprehensive survey, not limited to straightforward GANs related works, also including shallow neural networks and reinforcement learning. Concentrating on challenging face synthesis task, we summarize a stable training pipeline: 1) booting training procedure with noise injection; 2) fixing weights of fully connected layer in generator to improve performance further; 3) involving Markov decision module to dynamically choose learning rates of discriminator and generator respectively. Finally in experiments, we highlight a mutual evaluation criterion over entropy score based on a pre-trained classifier and manual voting.
A Case for Scoped Persist Barriers in GPUs

Two key trends in computing are evident --- emergence of GPU as a first-class compute element and emergence of byte-addressable nonvolatile memory technologies (NVRAM) as DRAM-supplement. GPUs and NVRAMs are likely to coexist in future systems. However, previous works have either focused on GPUs or on NVRAMs, in isolation. In this work, we investigate the enhancements necessary for a GPU to efficiently and correctly manipulate NVRAM-resident persistent data structures. Specifically, we find that previously proposed CPU-centric persist barriers fall short for GPUs. We thus introduce the concept of scoped persist barriers that aligns with the hierarchical programming framework of GPUs. Scoped persist barriers enable GPU programmers to express which execution group (a.k.a., scope) a given persist barrier applies to. We demonstrate that: 1 use of narrower scope than algorithmically-required can lead to inconsistency of persistent data structure, and 2 use of wider scope than necessary leads to significant performance loss (e.g., 25% or more). Therefore, a future GPU can benefit from persist barriers with different scopes.
Exploring synonyms as context in zero-shot action recognition

Zero shot learning (ZSL) provides a solution to recognising unseen classes without class labelled data for model learning. Most ZSL methods aim to learn a mapping from a visual feature space to a semantic embedding space, e.g. attribute or word vector spaces. The use of word vector space is particularly attractive as compared to attribute, it offers vast auxiliary classes with free parts embedding without human annotation. However, using the word vector embedding often provides weaker discriminative power than manually labelled attributes of the auxiliary classes. This is compounded further in zero-shot action recognition due to richer content variations among action classes. In this work we propose to explore a broader semantic contextual information in the text domain to enrich the word vector representation of action classes. We show through extensive experiments that this method improves significantly the performance of a number of existing word vector embedding ZSL methods. Moreover, it also outperforms attribute embedding ZSL with human annotation.
Enhanced light out-coupling from surface plasmonic loss minimized transparent organic light-emitting diodes

We report extremely high light out-coupling efficiency from a transparent organic light-emitting diode (OLED) integrated with microstructures on both sides of the device. [1] The OLED having a metal free structure offers dramatically reduced surface plasmonic loss and absorption loss. To extract the confined light inside the device, a high refractive index light extraction pattern was directly fabricated on the top side transparent conducting oxide electrode using a simple evaporation method, and a micro lens array sheet was simultaneously attached on the bottom side of the glass substrate. As a result, the external quantum efficiency of the device increased from 18.2% to 47.3% by using the microstructures, and was additionally enhanced to 62.9% by attaching an index-matched hemisphere lens instead of the micro lens array on the glass side in order to reduce additional light guiding loss inside of the device. These values showed very good agreement with the simulation performed by a combination of the dipole model and a 3-dimensional geometrical simulation.
Immunoproteomics: The Key to Discovery of New Vaccine Antigens Against Bacterial Respiratory Infections

The increase in antibiotic resistance and the shortage of new antimicrobials to prevent difficult bacterial infections underlines the importance of prophylactic therapies to prevent infection by bacterial pathogens. Vaccination has reduced the incidence of many serious diseases, including respiratory bacterial infections. However, there are many pathogens for which no vaccine is available and some vaccines are not effective among all age groups or among immunocompromised individuals. Immunoproteomics is a powerful technique which has been used to identify potential vaccine candidates to protect against pathogenic bacteria. The combination of proteomics with the detection of immunoreactive antigens using serum highlights immunogenic proteins that are expressed during infection. This is particularly useful when patient serum is used as the antigens that promote a humoral response during human infection are identified. This review outlines examples of vaccine candidates that have been identified using immunoproteomics and have successfully protected animals against challenge when tested in immunisation studies. Many immunoreactive proteins are common to several unrelated pathogens, however some of these are not always protective in animal immunisation and challenge studies. Furthermore, examples of well-established immunogens, including Bordetella pertussis antigen FHA were not detected in immunoproteomics studies, indicating that this technology may underrepresent the immunoreactive proteins in a pathogen. Although only one step in the pathway towards an efficacious approved vaccine, immunoproteomics is an important technology in the identification of novel vaccine antigens.
Performance enhancement of indoor VLC system by employing SAC OCDMA technique

In this paper, the indoor Visible Light Communication (VLC) system employing Spectral Amplitude Coding Optical Code Division Multiple Access (SAC OCDMA) technique is proposed and the performance of the proposed system is analyzed. SAC OCDMA is a multiplexing scheme that has become a research area of interest in Optical Wireless Communication. It has improved flexibility in channel allocation, ability to access asynchronously, enhanced privacy and increased network capacity. The performance of the indoor VLC System employing FBG based SAC OCDMA technique is compared with the existing VLC system employing intensity modulation/direct detection (IM/DD) technique. The results of this study show that the performance of the proposed system is better than the existing system in terms of quality factor, Bit error rate (BER), number of users, Transmission distance. By using this technique the maximum data rate of 600 Mbps and the maximum possible transmission distance of 600 m are achieved. The transmission of data with Quality factor 33 and Bit Error Rate 8e-150 is achieved.
Identifying the Critical Gaps in Research on Sex Differences in Metabolism Across the Life Span

The National Institutes of Health (NIH) Office of Research in Women's Health now functions under a mandate calling for the systematic inclusion of both female and male cells, animals, and human subjects in all types of research, so that sex as a biological variable is understood in health and disease. Sex-specific data can improve disease prevention, diagnosis, and treatment as well as reduce inequities. Inclusion of women in research studies has modestly improved over the last 20 years, yet preclinical research is still primarily done using male animal models and male-derived cells, with the result that many conclusions are made based on incomplete and sex-biased data. There are important, yet poorly studied, sex differences in cardiometabolic disease. To begin to address these sex differences, the Center for Women's Health Research at the University of Colorado held its inaugural National Conference, "Sex Differences Across the Lifespan: A Focus on Metabolism," in September 2016 (cwhr@ucdenver.edu). Research to address the important goal of understanding key sex differences in cardiometabolic disease across the life span is lacking. The goal of this article is to discuss the current state of research addressing sex differences in cardiometabolic health across the life span, to outline critical research gaps that must be addressed in response to NIH mandates, and, importantly, to develop strategies to address sex as a biological variable to understand disease mechanisms as well as develop diagnostic and therapeutic modalities.
IMCE: Energy-efficient bit-wise in-memory convolution engine for deep neural network

In this paper, we pave a novel way towards the concept of bit-wise In-Memory Convolution Engine (IMCE) that could implement the dominant convolution computation of Deep Convolutional Neural Networks (CNN) within memory. IMCE employs parallel computational memory sub-array as a fundamental unit based on our proposed Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) design. Then, we propose an accelerator system architecture based on IMCE to efficiently process low bit-width CNNs. This architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers and also accelerate CNN inference. The device to architecture co-simulation results show that the proposed system architecture can process low bit-width AlexNet on ImageNet data-set favorably with 785. 25μJ/img, which consumes ∼3× less energy than that of recent RRAM based counterpart. Besides, the chip area is ∼4× smaller.
Snacking Quality Is Associated with Secondary School Academic Achievement and the Intention to Enroll in Higher Education: A Cross-Sectional Study in Adolescents from Santiago, Chile

Although numerous studies have approached the effects of exposure to a Western diet (WD) on academic outcomes, very few have focused on foods consumed during snack times. We explored whether there is a link between nutritious snacking habits and academic achievement in high school (HS) students from Santiago, Chile. We conducted a cross-sectional study with 678 adolescents. The nutritional quality of snacks consumed by 16-year-old was assessed using a validated food frequency questionnaire. The academic outcomes measured were HS grade point average (GPA), the likelihood of HS completion, and the likelihood of taking college entrance exams. A multivariate analysis was performed to determine the independent associations of nutritious snacking with having completed HS and having taken college entrance exams. An analysis of covariance (ANCOVA) estimated the differences in GPA by the quality of snacks. Compared to students with healthy in-home snacking behaviors, adolescents having unhealthy in-home snacks had significantly lower GPAs (M difference: −40.1 points, 95% confidence interval (CI): −59.2, −16.9, d = 0.41), significantly lower odds of HS completion (adjusted odds ratio (aOR): 0.47; 95% CI: 0.25–0.88), and significantly lower odds of taking college entrance exams (aOR: 0.53; 95% CI: 0.31–0.88). Unhealthy at-school snacking showed similar associations with the outcome variables. Poor nutritional quality snacking at school and at home was associated with poor secondary school academic achievement and the intention to enroll in higher education.
Automating the Timed Up and Go Test Using a Depth Camera

Fall prevention is a human, economic and social issue. The Timed Up and Go (TUG) test is widely used to identify individuals with a high fall risk. However, this test has been criticized because its “diagnostic” is too dependent on the conditions in which it is performed and on the healthcare professionals running it. We used the Microsoft Kinect ambient sensor to automate this test in order to reduce the subjectivity of outcome measures and to provide additional information about patient performance. Each phase of the TUG test was automatically identified from the depth images of the Kinect. Our algorithms accurately measured and assessed the elements usually measured by healthcare professionals. Specifically, average TUG test durations provided by our system differed by only 0.001 s from those measured by clinicians. In addition, our system automatically extracted several additional parameters that allowed us to accurately discriminate low and high fall risk individuals. These additional parameters notably related to the gait and turn pattern, the sitting position and the duration of each phase. Coupling our algorithms to the Kinect ambient sensor can therefore reliably be used to automate the TUG test and perform a more objective, robust and detailed assessment of fall risk.
Diagnostics of severe acute respiratory syndrome-associated coronavirus (SARS-CoV) nucleocapsid antigen using chicken immunoglobulin Y

Abstract The goal of this study was to develop a quantitative detection system for severe acute respiratory syndrome-associated coronavirus (SARS-CoV), targeting the nucleocapsid protein (NP), to determine the presence and degree of infection in suspected individuals. Because the NP is the viral protein shed during infection and its template mRNA is the most abundant subgenomic RNA, it is a suitable candidate for developing antibodies for diagnostic applications. In this study, we have prepared full-length SARS-CoV NP expressed in Escherichia coli and purified. Full-length NP was used for the preparation of mouse monoclonal antibody and chicken polyclonal IgY antibodies for the development of heterosandwich ELISA for early diagnostics of SARS-suspected individuals. The sensitivity of the developed heterosandwich ELISA can detect the viral antigen at 18.5 pg/mL of recombinant NP. This study describes ultrasensitive ELISA using 19B6 monoclonal antibody as the capture antibody and IgY as the detecting antibody against the most abundant SARS-CoV NP antigens. One of the most important findings was the use of inexpensive polyclonal IgY antibody to increase the sensitivity of the detection system for SARS-CoV at the picogram level. Furthermore, the immunoassay of SARS-CoV NP antigen developed could be an effective and sensitive method of diagnosing SARS-suspected individuals during a future SARS-CoV outbreak.
Scaling behavior in interference lithography

Interference lithography is an emerging technology that provides a means for achieving high resolution over large exposure areas with virtually unlimited depth of field. 1D and 2D arrays of deep submicron structures can be created using near i-line wavelengths and standard resist processing. In this paper, we report on recent advances in the development of this technology, focusing in particular, on how exposure latitude and resist profile scale with interference period. We present structure width vs. dose curves for periods ranging from 200 nm to 1.0 micrometers , demonstrating that deep submicron structures can be generated with exposure latitudes exceeding 30 percent. Our experimental results are compared to simulations based on PROLITH/2.
Enlarging the Croatian WordNet with WN-Toolkit and Cro-Deriv

Wordnet is a standard semantic resource for several Natural Language Processing tasks and it is available for an increasing number of languages. The Croatian Wordnet (CroWN) was a relatively small resource with 10.026 synsets and 31.367 synset-variant pairs covering only 45.91% of the so-called Core WordNet. Comparing these figures with the size of the Princeton WordNet for English version 3.0, that has 117,659 synsets and 206,975 synset-variant pairs, it is clear that the CroWN should be expanded. First experiments for the expansion of the CroWN were performed using the WN-Toolkit, a set of Python programs for wordnet creation and expansion using dictionary, Babelnet and parallel-corpora based strategies. The WN-Toolkit was previously successfully applied to other languages as Spanish, Catalan and Galician. After this first expansion, CroWN reached 70.63% of the core wordnet. In the second step we used CroDeriv, a derivational database for Croatian and the manual creation of 1,457 synset-variant pairs until reaching 100% of the Core WordNet. After second step was completed, CroWN reached 23,137 synsets and 47,931 synset-lemma pairs.
Transcriptional regulation of the potential tumor suppressor ABI3 gene in thyroid carcinomas: interplay between methylation and NKX2-1 availability

We previously reported that ABI3 expression was decreased in thyroid cancer tissues and that ectopic expression of ABI3 in a follicular thyroid carcinoma cell line delayed cell cycle progression and inhibited cell proliferation, invasion, migration and tumor formation in athymic mice. These data indicated that ABI3 is a tumor suppressor gene; however the mechanism through which ABI3 is silenced in thyroid carcinomas is unknown. We here show that treatment of four follicular thyroid carcinoma cell lines with 5-aza-dC induced demethylation of a specific region of the ABI3 promoter and restored ABI3 expression. In contrast, 5-aza-dC treatment did not restore ABI3 expression in a non-thyroid cell line, suggesting a tissue-specific regulation. We additionally show that 8 CpG sites located within the ABI3 promoter are hypermethylated in most thyroid carcinoma samples and the degree of methylation correlated with ABI3 expression. Narrowing the region to specific CpG sites, the CpG4-6 sites showed the largest difference between benign and malignant lesions. In silico analysis revealed that these CpG sites flank a canonical binding site for NKX2-1, a thyroid specific transcriptional factor. Analysis of thyroid samples shows a correlation between NKX2-1 and ABI3 expression. In vitro assays demonstrate that NKX2-1 was required for ABI3 expression. Luciferase assay further confirmed the promoter activity of this region, which was increased when the cells were co-transfected with NKX2-1. Our study shows that the transcriptional silencing of ABI3 in cancer cells occurs via methylation and uncovered a previously unrecognized role for NKX2-1 in the regulation of ABI3.
Teatcups with automatic valves in machine milking of goats

This Research Paper addresses the hypothesis that using teatcups with automatic valves, without cutting off the vacuum prior to cluster removal, could increases the risk of mastitis and affect other milking variables on goats. A first trial used 46 intramammary infection (IMI)-free goats that had been milked with normal teatcups (without automatic valves) during a pre-experimental period of 8 ± 2 d postpartum. These animals were divided into two groups (n = 23), randomly assigning each group to teatcups with automatic valves (teatcups A) or without automatic valves (teatcups B) for a 20-week experimental period. During this period, several strategies were applied to increase teat exposure to pathogens in both experimental groups. In the first eight weeks of the experimental period, the new IMI rate per gland was significantly higher (P < 0·05) in the group of animals milked with teatcups A (6 of 46; 13%) than in the group milked with teatcups B (1 of 46; 2%). However, throughout the rest of the experimental period the same number of glands appeared with new IMI (n = 7) in both animal groups. SCC was higher in goats milked with teatcups A, but no significant differences were found in the remaining variables (milk production and composition, frequency of liner slips + teatcup fall-off). In a second experiment, in a crossover design (54 goats in fourth month of lactation, 2 treatments – teatcups A and B – in 2 experimental periods each lasting 1 week), no differences were observed in total milk, average milk flow, total milking time or teat thickness changes after milking between both teatcups. However, teatcups A worsened slightly the maximum milk flow. We concluded that the use of teatcups with automatic valves, without cutting off the vacuum prior to cluster removal, increases the risk of mastitis on goat livestock farms.
Hardware aware algorithm performance and the low power continuous wavelet transform

Highly miniaturised, wearable, physiological sensors require algorithms for the automated analysis of the collected signal. To reduce the total sensor power consumption in many situations the automated analysis is best carried on the sensor device itself and this online signal processing needs to be both accurate (in terms of correct detections and false detections) and also be implemented using very low power consumption circuits. However, reducing the circuit power consumption potentially impacts the algorithm performance. Hardware aware algorithms need to take this into account. This paper takes a previously reported 60 pW Continuous Wavelet Transform (CWT) circuit and investigates the impact of this circuit on a CWT-based algorithm for providing real-time EEG data reduction. An analytical model describing the measured variations in CWT response between different microchips is built, and this used in Matlab simulations of the EEG algorithm. Compared to using an ideal CWT stage, the impact of the modelled CWT circuit is negligible, resulting in only a 0.001 reduction in ROC-like performance area.