Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Compatibility of Reassortants between Avian H5N1 and H9N2 Influenza Viruses with Higher Pathogenicity in Mammals

Close interaction between avian influenza (AI) viruses and humans in Egypt appears to have resulted in many of the worldwide cases of human infections by both H5N1 and H9N2 AI viruses. Egypt is regarded as a hot spot of AI virus evolution. Although no natural reassortant of H5N1 and H9N2 AI viruses has been reported so far, their cocirculation in Egypt may allow emergence of reassortants that may present a significant public health risk. Using reverse genetics, we report here the first comprehensive data showing that H5N1-N9N2 reassortants have fairly high genetic compatibility and possibly higher pathogenicity in mammals, including humans, than the parental viruses. Our results provide insight into the emergence potential of avian H5N1-H9N2 reassortants that may pose a high public health risk. ABSTRACT The cocirculation of H5N1 and H9N2 avian influenza viruses in birds in Egypt provides reassortment opportunities between these two viruses. However, little is known about the emergence potential of reassortants derived from Egyptian H5N1 and H9N2 viruses and about the biological properties of such reassortants. To evaluate the potential public health risk of reassortants of these viruses, we used reverse genetics to generate the 63 possible reassortants derived from contemporary Egyptian H5N1 and H9N2 viruses, containing the H5N1 surface gene segments and combinations of the H5N1 and H9N2 internal gene segments, and analyzed their genetic compatibility, replication ability, and virulence in mice. Genes in the reassortants showed remarkably high compatibility. The replication of most reassortants was higher than the parental H5N1 virus in human cells. Six reassortants were thought to emerge in birds under neutral or positive selective pressure, and four of them had higher pathogenicity in vivo than the parental H5N1 and H9N2 viruses. Our results indicated that H5N1-H9N2 reassortants could be transmitted efficiently to mammals with significant public health risk if they emerge in Egypt, although the viruses might not emerge frequently in birds. IMPORTANCE Close interaction between avian influenza (AI) viruses and humans in Egypt appears to have resulted in many of the worldwide cases of human infections by both H5N1 and H9N2 AI viruses. Egypt is regarded as a hot spot of AI virus evolution. Although no natural reassortant of H5N1 and H9N2 AI viruses has been reported so far, their cocirculation in Egypt may allow emergence of reassortants that may present a significant public health risk. Using reverse genetics, we report here the first comprehensive data showing that H5N1-N9N2 reassortants have fairly high genetic compatibility and possibly higher pathogenicity in mammals, including humans, than the parental viruses. Our results provide insight into the emergence potential of avian H5N1-H9N2 reassortants that may pose a high public health risk.
Mapping tissue water T 1 in the liver using the MOLLI T 1 method in the presence of fat, iron and B 0 inhomogeneity

Modified Look‐Locker inversion recovery (MOLLI) T1 mapping sequences can be useful in cardiac and liver tissue characterization, but determining underlying water T1 is confounded by iron, fat and frequency offsets. This article proposes an algorithm that provides an independent water MOLLI T1 (referred to as on‐resonance water T1) that would have been measured if a subject had no fat and normal iron, and imaging had been done on resonance. Fifteen NiCl2‐doped agar phantoms with different peanut oil concentrations and 30 adults with various liver diseases, nineteen (63.3%) with liver steatosis, were scanned at 3 T using the shortened MOLLI (shMOLLI) T1 mapping, multiple‐echo spoiled gradient‐recalled echo and 1H MR spectroscopy sequences. An algorithm based on Bloch equations was built in MATLAB, and water shMOLLI T1 values of both phantoms and human participants were determined. The quality of the algorithm's result was assessed by Pearson's correlation coefficient between shMOLLI T1 values and spectroscopically determined T1 values of the water, and by linear regression analysis. Correlation between shMOLLI and spectroscopy‐based T1 values increased, from r = 0.910 (P < 0.001) to r = 0.998 (P < 0.001) in phantoms and from r = 0.493 (for iron‐only correction; P = 0.005) to r = 0.771 (for iron, fat and off‐resonance correction; P < 0.001) in patients. Linear regression analysis revealed that the determined water shMOLLI T1 values in patients were independent of fat and iron. It can be concluded that determination of on‐resonance water (sh)MOLLI T1 independent of fat, iron and macroscopic field inhomogeneities was possible in phantoms and human subjects.
The Relationship Between Sleep Characteristics and Unmet Physical Activity Need in Older Women

Objective: We examined among older women the association of sleep quality, daytime tiredness, and sleep duration with unmet physical activity need, that is, wishing to be more physically active but perceiving no opportunity for it. Method: Cross-sectional logistic regression analyses among women aged 74 to 86 years (Finnish Twin Study on Aging, third wave, n = 302). Results: Thirty-one participants reported unmet physical activity need. Short sleepers had fivefold and long sleepers threefold odds for unmet physical activity need compared with normative sleepers, while for daytime tiredness the odds were double. Presence of daytime tiredness and unmet physical activity coincided with higher prevalence of chronic diseases, depressive symptoms and walking difficulties, which partly explains the observed associations. Poor sleep quality was not associated with unmet physical activity need. Discussion: Older women with nonoptimal sleep characteristics who perceive unmet physical activity need may benefit from solutions that improve their perceived opportunities for physical activity.
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles

While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where small but carefully chosen perturbations to the input can make the models produce extremely inaccurate outputs. This makes these models particularly unsuitable for safety-critical application domains (e.g. self-driving cars) where robustness is extremely important. Recent work has shown that augmenting training with adversarially generated data provides some degree of robustness against test-time attacks. In this paper we investigate how this approach scales as we increase the computational budget given to the defender. We show that increasing the number of parameters in adversarially-trained models increases their robustness, and in particular that ensembling smaller models while adversarially training the entire ensemble as a single model is a more efficient way of spending said budget than simply using a larger single model. Crucially, we show that it is the adversarial training of the ensemble, rather than the ensembling of adversarially trained models, which provides robustness.
Understanding and Measuring Psychological Stress using Social Media

A body of literature has demonstrated that users’ mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how psychological stress is expressed on social media. Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions. In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data. Firstly, we find that stressed users post about exhaustion, losing control, increased self-focus and physical pain as compared to posts about breakfast, family-time, and travel by users who are not stressed. Secondly, we find that Facebook language is more predictive of stress than Twitter language. Thirdly, we demonstrate how the language based models thus developed can be adapted and be scaled to measure county-level trends. Since county-level language is easily available on Twitter using the Streaming API, we explore multiple domain adaptation algorithms to adapt user-level Facebook models to Twitter language. We find that domain-adapted and scaled social mediabased measurements of stress outperform sociodemographic variables (age, gender, race, education, and income), against ground-truth survey-based stress measurements, both at the user- and the county-level in the U.S. Twitter language that scores higher in stress is also predictive of poorer health, less access to facilities and lower socioeconomic status in counties. We conclude with a discussion of the implications of using social media as a new tool for monitoring stress levels of both individuals and counties.
Cytomorphologic diagnosis of inguinal node metastasis of Sertoli cell tumour

We present a rare case of cytologic diagnosis of Sertoli–Leydig cell tumour metastasis in the inguinal lymph nodes. The patient was primarily diagnosed with Sertoli cell tumour at the age of 35. Right adnexectomy was performed at the time. Total hysterectomy with contralateral adnexectomy at a University hospital was suggested after receiving the final histologic report but the patient declined the additional treatment. She was regularly followed for 3 y but stopped the follow-up thereafter of her own accord. Nine years after the primary diagnosis abdominal recurrence of the disease was discovered. Cytoreductive surgery was performed and recurrent Sertoli cell tumour of the ovary with necroses and a high mitotic activity (>7 mitoses per 10 high power fields) was confirmed on histopathology. The procedure was radical with no visible tumour left in the abdomen. At the age of 48, 4 y after the cytoreductive surgery for abdominal recurrence, the patient presented with vague lower abdominal pain and palpable right inguinal lymph node. Fine needle aspiration biopsy of the lymph node was performed. A highly cellular sample was obtained (Figure 1A) with single and grouped cells with uniform round and oval nuclei. Rosette-like structures were also visible (Figure 1B). The nuclei were not strongly hyperchromatic but had visible nucleoli in most cells. The cytoplasm was inhomogeneous, poorly circumscribed, and varying in amount. There were numerous naked nuclei without cytoplasm. There was a lot of extracellular hyaline substance that stained pink on Giemsa (Figure 1C) and blue on Papanicolaou staining (Figure 1D). On immunostaining the cells were positive for calretinin, CAM 5.2 and WT 1, and focally positive for CK AE1/AE3. Immunostaining for EMA was negative (Figure 2A-D). The cytological analysis confirmed a Sertoli cell tumour metastasis in the inguinal lymph node. The patient died soon after the fine needle aspiration biopsy due to the metastatic ovarian tumour with bone and liver metastases. Sertoli cell tumours are a rare form of sex cord-stromal tumours of the ovary and comprise less than 5% of these tumors.
The Case of Big Data Platform Services for Semiconductor Wafer Fabrication Foundries

We present in this paper two novel infrastructural services based on Hadoop for big data storage and computing in a Taiwan’s semiconductor wafer fabrication foundry. The two services include Hadoop data service (HDS) and distributed R language computing service (DRS), which have been built and operated in production systems for 3.5 years. They evolve over time by incrementally accommodating users’ requirements. HDS is a web- based distributed big data storage facility. Users simply rely on HDS to access data objects stored in Hadoop with the HTTP protocol. In addition, HDS is scalable and reliable. Moreover, HDS is efficient and effective by intelligently selecting either Hadoop distributed file system (HDFS) or database (HBase) for publishing data objects. Specifically, HDS is transparent to existing analytics and data inquiry applications, such as Spark and Hive. While HDS is a unified storage for supporting sequential and random data accesses for big data in the wafer fabrication foundry, DRS is a distributed computing framework for typical R language users. R users employ DRS to enjoy data-parallel computations, effortlessly and seamlessly. Similar to HDS, DRS can be horizontally scaled up. It guarantees the completion of computational jobs even with failures. In particular, it adaptively reallocates computational resources on the fly, minimizing job execution time and maximizing utilization of allocated resources. This paper1 discusses the design and implementation features for HDS and DRS.
Higher Proportion of Non-1-84 PTH Fragments in Peritoneal Dialysis Patients Compared to Hemodialysis Patients Using Solutions Containing 1.75 mmol/l Calcium

Background: The prevalence of low- turnover bone disease (LTBD) in peritoneal dialysis (PD) patients is higher than in hemodialysis (HD) patients. LTBD patients may be at risk for vascular calcification, and cardiovascular disease. Current therapy for chronic kidney disease metabolic bone disorders (CKD-MBD) is guided by biochemical parameters, as bone biopsy is not used in routine clinical care. Methods: We assessed intact PTH (iPTH: 1-84PTH plus non-1-84PTH), 1-84PTH, and the 1-84PTH/non-1-84PTH ratio in 129 hemodialysis and 73 PD prevalent patients dialyzed with solutions containing 1.75 mmol/L calcium. Results: Hemodialysis and PD patients presented similar iPTH and tCa values and prevalence of putative LTBD as defined according to KDOQI iPTH cut-off levels or 1-84 PTH levels. However, iCa accounted for a higher percentage of tCa in PD (53%) than in hemodialysis (39%) p < 0.001, and the 1-84PTH/non-1-84PTH ratio was lower in PD than in hemodialysis patients (0.44 ± 0.12) vs. (0.60 ± 0.10), p < 0.001. The prevalence of putative LTBD when using the coexistence of 1-84PTH/non-1-84PTH ratio < 1.0 and iPTH < 420 pg/m, was higher in PD than in hemodialysis patients (73 vs. 16% respectively, p < 0.001). In a multivariate logistic regression analysis, dialysis modality was the main determinant of the 1-84PTH/non-1-84PTH ratio. Conclusion: Solutions containing 1.75 mmol/L calciums are associated to a higher proportion of non-1-84PTH fragments in PD than in HD patients. Different analytical criteria result in widely different estimates of LTBD prevalence, thus impairing the ability of clinicians to optimize therapy for CKD-MBD.
A Flexible Classifier Based on Optimum Curve Fitting Approach

ABSTRACT This study proposes a curve fitting approach for classification problems. The different classification data sets are utilized to test and evaluate the suggested method. For tested classification problems, the Gaussian curve fitting models are used. In the curve fitting stage, the number of curves equals the number of attributes in the related classification problem. For example, there are 4 attributes for iris dataset, thus four Gaussian curves are fitted for this problem. Then, output values of these fitted curves are calculated to average values, and this average value is rounded to the nearest integers. The same procedure is applied to the other dataset with having different number of features. In optimization stage, for each of classification application, the optimum values of constants of Gaussian function are determined by using genetic algorithm. For all used classification dataset, a part of the set is used during the optimization phase, and then the proposed model is validated with the remainder of the dataset. Furthermore, the optimal valuesof each of the attributes in tested classification application are determined by optimization algorithm. It is a valuable property of the proposed method that the accuracy of high classification can be achieved with a low number of reference data by the stage of determination of optimal feature set. Simulation results show that proposed classification approach with optimum values of constants and optimal feature set based on curve fitting has high accuracy rate. The proposed approach can be used for different classification problems.
Hyperpolarized 13C magnetic resonance imaging, using metabolic imaging to improve the detection and management of prostate, bladder, and kidney urologic malignancies

Approximately 25% of the 2 million new cancer diagnoses in the United States in 2018 were comprised of malignancies of the urogenital system. Of these cancers, 75% occurred in the kidney/renal pelvis, prostate, and urinary bladder. Early diagnosis is beneficial to long-term survival. Currently, urologists rely heavily on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and positron emission tomography (PET) to both diagnose and offer prognoses, but these techniques are limited in their resolution and are more effective when cancers have reached macroscopic size in later stages. Recent developments in cancer metabolomics have revealed that cancerous cells preferentially upregulate specific metabolic pathways as a means of conserving their resources and maximizing their growth potential. This has opened a new avenue for early diagnosis with much higher resolution, reliability, and accuracy through 13C hyperpolarized MRI. Preferential cancer pathways can be elucidated through this technique using 13C-labeled molecules utilized for energy generation and tumor growth. As these pathways are identified, targeted therapies are being designed to inhibit these pathways to allow for treatment that is cytotoxic to malignant cells but preserves native cells. In this paper, we review the current understanding of urologic cancer metabolomics, specifically in the kidney, prostate, and bladder. We will review the basic physics of MRI and demonstrate how hyperpolarized 13C MRI offers an innovative solution to early diagnosis as well as creates novel avenues for more targeted therapy.
An Empirical Study on Software Aging Indicators Prediction in Android Mobile

The requirements for high reliability, availability, and performance of mobile devices have increased significantly. Android is the most widely used mobile operating system in the world, and it is affected by software aging, resulting in poor responsiveness. This paper investigates the software aging indicators prediction in Android, focusing on aging indicators such as system's free physical memory and application's heap memory. Due to the various user behavior sequences for Android applications and system, we utilize Long Short-Term Memory Neural Network (LSTM NN), which could capture the hidden long-term dependence in a time series to predict these aging indicators. We analyze the prediction results with traditional evaluation metrics like MAPE/MSE for evaluating the whole prediction performance, and with our proposed evaluation metrics TA, FA, SVA for evaluating the trend, fluctuation, and small variation of aging indicators respectively. The results show that LSTM NN has superior performance compared with other prediction methods in the history of software aging researches. Based on the results, proactive management techniques like software rejuvenation could be scheduled by predicting the proper moment to alleviate software aging effects and increase the availability of Android mobile.
Metabolic cost underlies task-dependent variations in motor unit recruitment

Mammalian skeletal muscles are comprised of many motor units, each containing a group of muscle fibres that have common contractile properties: these can be broadly categorized as slow and fast twitch muscle fibres. Motor units are typically recruited in an orderly fashion following the ‘size principle’, in which slower motor units would be recruited for low intensity contraction; a metabolically cheap and fatigue-resistant strategy. However, this recruitment strategy poses a mechanical paradox for fast, low intensity contractions, in which the recruitment of slower fibres, as predicted by the size principle, would be metabolically more costly than the recruitment of faster fibres that are more efficient at higher contraction speeds. Hence, it would be mechanically and metabolically more effective for recruitment strategies to vary in response to contraction speed so that the intrinsic efficiencies and contraction speeds of the recruited muscle fibres are matched to the mechanical demands of the task. In this study, we evaluated the effectiveness of a novel, mixed cost function within a musculoskeletal simulation, which includes the metabolic cost of contraction, to predict the recruitment of different muscle fibre types across a range of loads and speeds. Our results show that a metabolically informed cost function predicts favoured recruitment of slower muscle fibres for slower and isometric tasks versus recruitment that favours faster muscles fibres for higher velocity contractions. This cost function predicts a change in recruitment patterns consistent with experimental observations, and also predicts a less expensive metabolic cost for these muscle contractions regardless of speed of the movement. Hence, our findings support the premise that varying motor recruitment strategies to match the mechanical demands of a movement task results in a mechanically and metabolically sensible way to deploy the different types of motor unit.
Overexpression of the Wheat (Triticum aestivum L.) TaPEPKR2 Gene Enhances Heat and Dehydration Tolerance in Both Wheat and Arabidopsis

Wheat (Triticum aestivum L.) yield and quality are adversely affected by heat, drought, or the combination of these two stresses in many regions of the world. A phosphoenolpyruvate carboxylase kinase-related kinase gene, TaPEPKR2, was identified from our previous heat stress-responsive transcriptome analysis of heat susceptible and tolerant wheat cultivars. Based on the wheat cultivar Chinese Spring genome sequence, TaPEPKR2 was mapped to chromosome 5B. Expression analysis revealed that TaPEPKR2 was induced by heat and polyethylene glycol treatment. To analyze the function of TaPEPKR2 in wheat, we transformed it into the wheat cultivar Liaochun10, and observed that the transgenic lines exhibited enhanced heat and dehydration stress tolerance. To examine whether TaPEPKR2 exhibits the same function in dicotyledonous plants, we transformed it into Arabidopsis, and found that its overexpression functionally enhanced tolerance to heat and dehydration stresses. Our results imply that TaPEPKR2 plays an important role in both heat and dehydration stress tolerance, and could be utilized as a candidate gene in transgenic breeding.
Kernel-Based Training of Generative Networks

Generative adversarial networks (GANs) are designed with the help of min-max optimization problems that are solved with stochastic gradient-type algorithms which are known to be non-robust. In this work we revisit a non-adversarial method based on kernels which relies on a pure minimization problem and propose a simple stochastic gradient algorithm for the computation of its solution. Using simplified tools from Stochastic Approximation theory we demonstrate that batch versions of the algorithm or smoothing of the gradient do not improve convergence. These observations allow for the development of a training algorithm that enjoys reduced computational complexity and increased robustness while exhibiting similar synthesis characteristics as classical GANs.