Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein-energy malnutrition during early gestation in sheep blunts fetal renal vascular and nephron development and compromises adult renal function

Non‐technical summary A poor diet during pregnancy has been linked to long‐term health outcomes for the baby, such as an increased risk of diseases of the heart and kidney. We show in an experimental model that recreates a poor diet during pregnancy, i.e. a diet low in protein with adequate energy, that kidney development in the baby is affected in such a way as to reduce the potential for new blood vessels to form. This results in a greater number of important, functional kidney cells spontaneously dying. Later in life, these effects in the kidney manifest as permanently reduced kidney function, especially if the baby subsequently becomes overweight as an adult. The research reinforces advice to pregnant mothers about the importance of eating a nutritionally balanced diet during pregnancy.
Update from Sri Lankan Twin Registry: establishment of a population-based twin register and ongoing project on common mental disorders, alcohol abuse and suicidal ideations.

The Sri Lankan Twin Registry began as a volunteer register in 1997. Previously, we have shown door-to-door surveys as the best option to build a population-based twin register of older, adult twins. Our aim was to build a population-based twin register in the Colombo district and use it for twin studies on common mental disorders. We aimed to identify a random sample of 4000 twins ascertained through the twin census. The Colombo district is divided into 13 divisional secretariat divisions (DSDs) and each division is further divided into Grama Niladari divisions administratively. Grama niladaris (GNs) are civil servants and visit each household in order to update the electoral register. GNs were used to do a census of twins while they updated the electoral register. The correlation between population density and twin rate among DSDs was . 81, and between twin rate and the percentage of returned forms (from each DSD) was . 74. We received 9648 forms notifying about twins and multiples. After removing the duplicates and information about twins who live in other districts, there were 17,406 multiples remaining. After incorporating twins and multiples from various other feasibility studies we now have 19,040 multiples with 8.46 twins per 1000 people in the Colombo district.
A Heuristic Method of Attack Graph Analysis for Network Security Hardening

Traditional vulnerability scan tools cannot show the associations among vulnerabilities, and thus the security administrators have the difficulty to comprehensively understand the risks in networks according to the vulnerabilities sources. With the number of vulnerabilities growing rapidly, repairing all vulnerabilities costs much. In order to mitigate this problem, we propose a method using attack graph analysis, which provides network security hardening strategies in a cost effective way. For such a purpose, we construct attack graphs by software, and analyze the potential risks in networks by preprocessing them. Further, we calculate low-cost network security hardening strategies via modified ant-colony optimization. In case that the algorithm falls into local optima, a node-hidden mechanism with the highest selected probability is introduced. We have evaluated the performance of the proposed algorithm by simulations. The experimental results show that this method achieves scalability and significantly reduces cost of network security hardening strategies in an acceptable running time. Keywords-vulnerability; attack graph; network security hardening; ant colony optimization
Representing Cloud Water Content of Extensive Cloud Systems Over Land Using Satellite‐Based Passive Microwave Observations With a Coupled Land and Atmosphere Assimilation Method

We proposed a method to estimate cloud water content (CWC) over land using satellite‐based passive microwave brightness temperatures (TBs) at multiple‐kilometer resolutions with land and atmosphere assimilation to overcome the challenges associated with estimating the CWC of broad cloud systems over land. This method enables estimation of broad cloud systems over land using multifrequency TBs, which have different sensitivities to land and cloud, by concurrently optimizing land emissions and CWC estimates from models. Estimated CWC was validated using vertical two‐dimensional CloudSat products (2B‐CWC‐RVOD and 2C‐ICE). The results were in good accordance with 2B‐CWC‐RVOD in terms of cloud water path and the vertical distribution of CWC but represented underestimates in comparison to 2C‐ICE. We performed sensitivity analysis of CWC estimates of TBs and cloud top height. The results suggested that the error in TBs is not large uncertainty, and that cloud top height affects the estimated CWC more sensitively than TBs. The addition of cloud top height information, as determined from CloudSat products as a constraint of optimization, allows further improvement of the vertical distributions of CWC in case studies. Sensitivity analysis indicated that it is effective to utilize cloud top height data from other satellites, such as next‐generation geostationary meteorological satellites, within an error of about ±600 m for further development of our method. This study revealed that the proposed method has great potential to provide unprecedented data for cloud water path and CWC that are continuously distributed over land and ocean with adequate accuracy.
Maximum Likelihood and Restricted Likelihood Solutions in Multiple-Method Studies

A formulation of the problem of combining data from several sources is discussed in terms of random effects models. The unknown measurement precision is assumed not to be the same for all methods. We investigate maximum likelihood solutions in this model. By representing the likelihood equations as simultaneous polynomial equations, the exact form of the Groebner basis for their stationary points is derived when there are two methods. A parametrization of these solutions which allows their comparison is suggested. A numerical method for solving likelihood equations is outlined, and an alternative to the maximum likelihood method, the restricted maximum likelihood, is studied. In the situation when methods variances are considered to be known an upper bound on the between-method variance is obtained. The relationship between likelihood equations and moment-type equations is also discussed.
Building an automated classification of DNA-binding protein domains

Intensive growth in 3D structure data on DNA-protein complexes as reflected in the Protein Data Bank (PDB) demands new approaches to the annotation and characterization of these data and will lead to a new understanding of critical biological processes involving these data. These data and those from other protein structure classifications will become increasingly important for the modeling of complete proteomes. We propose a fully automated classification of DNA-binding protein domains based on existing 3D-structures from the PDB. The classification, by domain, relies on the Protein Domain Parser (PDP) and the Combinatorial Extension (CE) algorithm for structural alignment. The approach involves the analysis of 3D-interaction patterns in DNA-protein interfaces, assignment of structural domains interacting with DNA, clustering of domains based on structural similarity and DNA-interacting patterns. Comparison with existing resources on describing structural and functional classifications of DNA-binding proteins was used to validate and improve the approach proposed here. In the course of our study we defined a set of criteria and heuristics allowing us to automatically build a biologically meaningful classification and define classes of functionally related protein domains. It was shown that taking into consideration interactions between protein domains and DNA considerably improves the classification accuracy. Our approach provides a high-throughput and up-to-date annotation of DNA-binding protein families which can be found at http://spdc.sdsc. edu.
On Decision Boundaries of Na¨ıve Bayes in Continuous Domains

Na¨ıve Bayesian classiﬁers assume the conditional independence of attribute values given the class. Despite this in practice often violated assumption, these simple classiﬁers have been found eﬃcient, eﬀective, and robust to noise. Discretization of continuous attributes in na¨ıve Bayesian classiﬁers has achieved a lot of attention recently. Continuous attributes need not necessarily be discretized, but it uniﬁes their handling with nominal attributes and can lead to improved classiﬁer performance. We show that optimal partitioning results from decision tree learning carry over to Na¨ıve Bayes as well. In particular, it sets decision boundaries on borders of segments with equal class frequency distribution. An optimal univariate discretization with respect to the Na¨ıve Bayes rule can be found in linear time but, unfortunately, optimal multivariate optimization is intractable.
Preparation for the Final Testing of the Sprayed Bentonite Sealing Layer of a Pond Dam

This paper presents research conducted on the development of a bentonite material suitable for the sealing of the dams of large ponds used for commercial fishing purposes. It is intended that the material will be applied using spray technology. The research was motivated by the large number of very old commercial fish ponds in the Czech Republic of which some are nearly a thousand years old. With the passage of time, leakage through the dams leads to the creation of caverns and preferential flow paths which may eventually result in a breach in the dam wall. An efficient technological approach to the sealing of these earth structures may, therefore, help to mitigate the risk of their failure and destruction. A potentially suitable material for this purpose has been researched and developed at the Czech Technical University and is now ready for the experimental testing phase involving the construction and study of a physical model of such a dam constructed according to the geometry of traditional commercial fish ponds.
An Immunohistochemical Approach to Monitor the Prolactin-induced Activation of the JAK2/STAT5 Pathway in Pancreatic Islets of Langerhans

This study examined whether an immunohistochemical method examining the subcellular localization of STAT5 could be used to characterize the activation of the JAK2/STAT5 pathway by prolactin (PRL) in intact cells or tissues. In the Ins-1 β-cell line, STAT5A and STAT5B were distributed almost equally in the cytoplasm and the nucleus in unstimulated cells. STAT5A was also detected along the border of cells and in the perinuclear region. After exposure to PRL, the redistribution from the cytoplasm to the nucleus was much higher for STAT5B compared to STAT5A. This translocation represented 12% of the STAT5A and 22% of the STAT5B originally located in the cytoplasm before stimulation. In isolated rat islets of Langerhans, PRL stimulated the nuclear translocation of both STAT5A and STAT5B only in β-cells. The expression of the PRL receptor only by β-cells was confirmed with a rabbit polyclonal antiserum raised against the rat PRL receptor. It was estimated that 4% of STAT5A and 9% of STAT5B originally located in the cytoplasm was translocated to the nucleus after stimulation. The presence of a functional JAK2/STAT5 signaling pathway in all islet cells was demonstrated by the nuclear translocation of STAT5B in all islet cells (i.e., α-, β-, and δ-cells) after stimulation with fetal calf serum. The nuclear translocation and tyrosine phosphorylation of STAT5B was biphasic, with an initial peak within 30 min, a nadir between 1 and 3 hr, and prolonged activation after 4 hr. In contrast, the tyrosine phosphorylation of STAT5A was also biphasic but its nuclear translocation peaked within 30 min and was then reduced to a level slightly above that observed before PRL stimulation. This method is able to detect changes in STAT5 activation as small as 2% of the total cell content. These observations demonstrate the utility of this approach for studying the activation of STAT5 in a mixed population of cells within tissues or organs. In addition, the dose response for the nuclear translocation of STAT5B in normal β-cells was similar to those for changes in proliferation and insulin secretion in isolated rat islets. Therefore, the subcellular localization can be used to monitor the activation of STAT5 and it may be a key event in the upregulation of the pancreatic islets of Langerhans during pregnancy.
Polar Decomposition Based Hybrid Beamforming Design for mmWave Massive MIMO Systems

This paper considers hybrid beamforming (HBF) for the point-to-point (P2P) millimeter wave (mmWave) massive MIMO systems. The optimal hybrid precoding and combining matrices that maximizes the system capacity can be obtained based on the singular value decomposition (SVD) of the channel matrix. Then, the optimal unconstrained hybrid digital and analog precoders (combiners) are designed according to the polar decomposition of the optimal hybrid precoding (combining) matrix. Considering the actual hardware constraints, we propose a joint transmitter and receiver HBF algorithm based upon polar decomposition. In this algorithm, the hybrid analog constrained precoding and combining matrices can be derived without having to incur an excessive computational complexity of an iterative approach. Simulation results show that the proposed algorithm can approach the performance of optimal unconstrained precoding, and is insensitive to the accuracy of the channel state information (CSI).
SEEDrive — Using in-car serial-bus systems for safe and energy efficient driving

Beside the technical optimisation of the vehicle, energy-efficiency and road safety can be increased by optimising the driving behaviour as well. Based on this fact SEEDrive has the goal to educate the driver in energy-efficient and safe driving. In this paper the Data Layer of SEEDrive is introduced, which is responsible for the data acquisition and preparation as well as the generation of the driving profile. The information from the car, the driver and the environment is gathered using the in-car serial-bus systems as well as vital- and environment sensors. The incoming data is then aggregated using fuzzy logic for further processing. Finally, the Data Layer generates a driving profile, which represents the normal driving behaviour of the driver. On the basis of the information provided by the Data Layer, SEEDrive adapts itself to the individual driving behaviour and gives adequate recommendations, regarding energy-efficiency and safety. This allows eliminating bad driving habits, while considering the driver needs.
A coverage algorithm for intermittently connected delay tolerant wireless sensor networks

This work presents a packet drop solution applied to delay tolerant wireless sensor networks. In such networks, when the storage capacity of a node is limited compared to the amount of data to collect, it is common to apply some packet drop strategy based on network layer aspects only. However, such strategies are not suitable for monitoring applications of WSNs where data quality is essential. Alternatively, we propose a drop policy based on global network coverage where the packets that contribute less to the overall network view are chosen to be dropped. In order to evaluate our solution, we executed and compared it with other algorithms in literature. We modelled and simulated scenarios where different events occur. The results show that the algorithm based on network coverage is better than common drop strategies.
An incentive-based fairness mechanism for multi-hop wireless backhaul networks with selfish nodes

In this paper, we study the fairness problem in multi-hop wireless backhaul networks in the presence of selfish transit access points (TAPs). We design an incentive-based mechanism which encourages TAPs to forward data for other TAPs, and thus eliminates the location-dependent unfairness problem in the backhaul network. We prove the correctness and truthfulness of the proposed mechanism, and evaluate its performance via ns-2 simulations. The results show that the proposed mechanism achieves fairness even when there are idle TAPs in the network.
Spectrophotometric Determination of Ciprofloxacin Hydrochloride in Ophthalmic Solution

The objective o f this study was to develop simple and rapid method for the determination of cipro flo xacin hydrochloride in ophthalmic solution using UV spectrophotometry. The intrinsic absorbance of ciproflo xacin was measured directly at 275 n m. The calib ration graph was linear over the range 2.0- 7.0 µg mL -1 with relative standard deviations (RSD) values ranging from 1.55 to 2.47% (n =6). The method was validated through the parameters of linearity, accuracy, precision, limit of detection, limit of quantitation,specificity and robustnessaccording to ICH. The proposed method is enabled a quantitative determination of ciproflo xacin hydrochloride in ophthalmic solution.
Security Concerns in Android mHealth Apps

Mobile Health (mHealth) applications lie outside of regulatory protection such as HIPAA, which requires a baseline of privacy and security protections appropriate to sensitive medical data. However, mHealth apps, particularly those in the app stores for iOS and Android, are increasingly handling sensitive data for both professionals and patients. This paper presents a series of three studies of the mHealth apps in Google Play that show that mHealth apps make widespread use of unsecured Internet communications and third party servers. Both of these practices would be considered problematic under HIPAA, suggesting that increased use of mHealth apps could lead to less secure treatment of health data unless mHealth vendors make improvements in the way they communicate and store data.
Detecting Interesting Event Sequences for Sports Reporting

Hand-crafted approaches to content determination are expensive to port to new domains. Machine-learned approaches, on the other hand, tend to be limited to relatively simple selection of items from data sets. We observe that in time series domains, textual descriptions often aggregate a series of events into a compact description. We present a simple technique for automatically determining sequences of events that are worth reporting, and evaluate its effectiveness.
Comparison of performance of enhanced morpheme-based language model with different word-based language models for improving the performance of Tamil speech recognition system

This paper describes a new technique of language modeling for a highly inflectional Dravidian language, Tamil. It aims to alleviate the main problems encountered in processing of Tamil language, like enormous vocabulary growth caused by the large number of different forms derived from one word. The size of the vocabulary was reduced by, decomposing the words into stems and endings and storing these sub word units (morphemes) in the vocabulary separately. A enhanced morpheme-based language model was designed for the inflectional language Tamil. The enhanced morpheme-based language model was trained on the decomposed corpus. The perplexity and Word Error Rate (WER) were obtained to check the efficiency of the model for Tamil speech recognition system. The results were compared with word-based bigram and trigram language models, distance based language model, dependency based language model and class based language model. From the results it was analyzed that the enhanced morpheme-based trigram model with Katz back-off smoothing effect improved the performance of the Tamil speech recognition system when compared to the word-based language models.
Derivation and validation of cystatin C-based prediction equations for GFR in children.

BACKGROUND Cystatin C (CysC) may be a better marker of glomerular filtration rate (GFR) than serum creatinine (SCr) level. Few studies derived CysC-based GFR prediction equations for children. Objectives of this study are to: (1) derive CysC-based GFR prediction equations for children, and (2) compare these equations with published formulae. METHODS Patients younger than 18 years undergoing iothalamate GFR (IoGFR) testing were studied prospectively. Data collected were age, sex, CysC level, SCr level, IoGFR, height, weight, and diagnosis. By using linear regression, 2 equations were derived and compared with 3 previously published formulae by using Bland-Altman analysis and diagnostic characteristics. Local coefficients were derived for comparison formulae. RESULTS There were 111 GFR tests from 103 patients (age, 12.7 +/- 4.7 years; IoGFR, 73.6 +/- 35.7 mL/min/1.73 m(2) [1.23 +/- 0.60 mL/s/1.73 m(2)]; 60% male; and 25% post-renal transplantation). The 2 equations derived were the CysEq (including CysC level) and the CysCrEq (including CysC and SCr levels). Overall, the 2 new equations had bias and precision similar to previously published formulae when local coefficients were used. However, in patients with a renal transplant or spina bifida, the 2 new equations were less biased and more precise. All CysC-based equations performed better than the Schwartz formula. CONCLUSION This study provides 2 CysC-based GFR prediction equations that are accurate, precise, and sensitive for detecting abnormal GFRs. Three previously published CysC GFR prediction equations have been validated for the first time. Prediction equations based on CysC level are likely to provide more accurate estimates of GFR than SCr-based equations.
Risk Factors for Severe Cholera among Children under Five in Rural and Urban Bangladesh, 2000–2008: A Hospital-Based Surveillance Study

Background Children under five bear the largest cholera burden. We therefore sought to identify modifiable risk factors among Bangladeshi children. Methodology/Principal Findings We used multivariate Poisson regression to assess risk factors for severe cholera among diarrheal patients presenting at hospitals in Matlab (rural) and Dhaka (urban), Bangladesh. Risk increased with age. Compared to those under one, rural and urban four-year-olds had adjusted risk ratios (aRR) of 4.17 (95% confidence interval (CI) 2.43–7.15) and 6.32 (95% CI: 4.63–8.63), respectively. Breastfeeding halved the risk in both rural (aRR = 0.49, 95% CI: 0.35–0.67) and urban (aRR = 0.51, 95% CI: 0.41–0.62) settings. Rural children’s risk decreased with maternal education (P-trend: <0.001) and increased among those with a family member with diarrhea in the past week (aRR = 1.61, 95% CI: 1.22–2.14) and those with prior vitamin A supplementation (aRR = 1.65, 95% CI: 1.12–2.43). Urban children whose mothers daily (aRR = 0.41, 95% CI: 0.21–0.79) or occasionally (aRR = 0.55, 95% CI: 0.36–0.84) read a newspaper experienced reduced risk. Urban children from households with incomes between 34–84 USD/month had a 30% increased risk compared to those from households with incomes >84 USD/month. Conclusion/Significance Increasing age, lower socioeconomic status, and lack of breastfeeding are key correlates of increased risk for cholera hospitalization among those under five in rural and urban Bangladesh. In addition, having a family member with diarrhea in the past week was associated with increased risk among rural children. Continued attention should be directed to the promotion of breastfeeding. Further research is needed to elucidate the relationship between maternal education and cholera risk. Renewed research regarding the use of chemoprophylaxis among family members of cholera cases may be warranted in rural endemic settings.