Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiation of Primary Central Nervous System Lymphomas and Glioblastomas: Comparisons of Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging without and with Contrast-Leakage Correction

BACKGROUND AND PURPOSE: Contrast leakage results in underestimation of the CBV of brain tumors. Our aim was to compare the diagnostic performance of DSC perfusion MR imaging without and with mathematic contrast-leakage correction in differentiating PCNSLs and glioblastomas. MATERIALS AND METHODS: Perfusion parameters—CBV, corrected CBV, and leakage coefficient—were measured in enhancing tumor portions and contralateral NAWM of 15 PCNSLs and 20 glioblastomas, respectively. The ratios of CBV and corrected CBV were calculated by dividing the tumor values by those obtained from contralateral NAWM. A paired t test was used to compare tumor K2 and NAWM K2, as well as tumor CBV ratios without and with leakage correction. Comparisons of CBV, corrected CBV, and K2 between PCNSLs and glioblastomas were done by using a 2-sample t test. The diagnostic performance of DSC perfusion MR imaging without and with contrast-leakage correction was assessed with receiver operating characteristic curve analysis. RESULTS: PCNSLs and glioblastomas demonstrated higher K2 than those in their contralateral NAWM. Corrected CBV ratios were significantly higher than the uncorrected ones for both tumors. PCNSLs had lower CBV ratios (P < .001), lower corrected CBV ratios (P < .001), and higher K2 (P = .001) compared with glioblastomas. In differentiating between PCNSLs and glioblastomas, the area under the curve of the CBV ratio, corrected CBV ratio, and K2 were 0.984, 0.940, and 0.788, respectively. CONCLUSIONS: PCNSL can be differentiated from glioblastoma with CBV ratios, corrected CBV ratios, and K2. CBV without contrast-leakage correction seems to have the best diagnostic performance in differentiating the 2 tumors.
Effect of nano-zinc oxide on doxorubicin- induced oxidative stress and sperm disorders in adult male Wistar rats

Background: Doxorubicin (DOX), an anthracycline antibiotic, is a widely used anticancer agent. In spite of its high antitumor efficacy, the use of DOX in clinical chemotherapy is limited due to diverse toxicities, including gonadotoxicity. Objective: We investigated the protective effect of nano-zinc oxide (nZnO) as an established antioxidant on DOX-induced testicular disorders. Materials and Methods: In this experimental study 24 adult male Wistar rats were divided into four groups including one control and three experimentals (6 rats per group). They received saline (as control), DOX alone (6 mg/kg body weight, i.p.), nZnO alone (5 mg/kg body weight, i.p.), and nZnO followed by DOX. Animals were sacrificed 28 days after treatment and evaluations were made by sperm count and measuring sex hormone levels in plasma. Also total antioxidant power (TAP) and lipid peroxidation (LPO) in plasma were tested. Data was analyzed with SPSS-14 and one way ANOVA test. P<0.05 were considered to be statistically significant. Results: In the DOX-exposed rats significant differences were found compared with the control group (p=0.001) in plasma total antioxidant power (TAP) (425.50±32.33 vs. 493.33±18.54 mmol/mL), Lipid peroxidation (LPO) (3.70±0.44 vs. 2.78±0.68 μmol/mL), plasma testosterone (3.38±0.69 vs. 5.40±0.89 ng/dl), LH (0.26±0.05 vs. 0.49±0.18 mlU/mL), sperm count (157.98±6.29 vs. 171.71±4.42×106/mL) and DNA damage (11.51±3.45 vs. 6.04±2.83%). Co-administration of nZnO significantly improved DOX-induced changes (p=0.013) in plasma TAP (471.83±14.51 mmol/mL), LPO (2.83±0.75 μmol/mL), plasma testosterone (5.00±1.07 ng/dl), LH (0.52±0.08 mlU/mL), sperm count (169.13±5.01×106/mL) and DNA damage (7.00±1.67%). Conclusion: At the dose designed in the present investigation cytoprotective role of nano-zinc oxide through its antioxidant potential is illuminated in DOX-induced male gonadotoxicity.
Efficient Calculation of Quasi-Bound State Tunneling in CMOS Devices

For an accurate description of direct tunneling in CMOS devices under inversion conditions lifetime based approaches rely on the precise determination of quasi-bound states (QBS). We study the calculation of QBS in resonant tunneling diode (RTD) structures and MOS inversion layers by the perfectly matched layer ((PML) method. Introducing a complex coordinate stretching allows to apply artifical absorbing layers at the boundaries. The QBS appear as the eigenvalues of a linear non-Hermitian Hamiltonian where the QBS lifetimes are directly related to the imaginary part of the eigenvalues. The PML formalism has been compared to the established quantum transmitting boundary method where a computationally demanding scanning procedure yields the desired lifetimes. The PML method proves as an elegant, numerical stable, and efficient method to calculate QBS lifetimes.
Epilepsy in Nepal

Abstract This is a review of epilepsy in Nepal. Nepal is a predominantly rural country with a population of 21 million. A community-based survey in Morang district showed that the prevalence of epilepsy was 7.3 per 1,000 population. A cohort of 300 cases in Shree Birendra Military Hospital showed that neurocysticercosis was the most important etiological cause. There are seven neurologists, 10 CT scanners, three MRIs, and four EEG machines in the country. The practice of anticonvulsant use varied according to different geographical locations. The treatment gap may be in excess of 70%. The epidemiology study in Morang district showed that the proportion seeking modern treatment was higher among those with more frequent seizure. None of the patients were able to attribute their illness to brain disease. Various forms of traditional treatment are widely practiced in all casts among Hindus as well as Buddhists.
Performance Evaluation of Voice Call over an IP based Network

As mobile communication involves transferring an ongoing call from one cell to another as the user moves through the coverage area, it is important to study the factors that affect the overall performance. This paper presents the process of voice over Internet Protocol (VoIP) in a third generation (3G) network with performance was evaluation. The paper also discusses the quality of voice when compression technique is used to reduce the amount of required bandwidth. Each packet takes a different amount of time to go from the source to its destination. The actual voice packet arrival is estimated and the delay from source to destination is calculated. The results show the impact of packet arrival rate and traffic load on the overall performance.
Prevalence of Tobacco Smoking and Determinants of Success in Quitting Smoking among Patients with Chronic Diseases: A Cross-Sectional Study in Rural Western China

Tobacco use is one of the behavioral risk factors for chronic diseases. The aim of the study was to investigate smoking prevalence in chronically ill residents and their smoking behavior in western rural China, to identify factors associated with success in quitting smoking, and to provide appropriate intervention strategies for tobacco control. Cross-sectional survey data from patients with chronic diseases from rural western China were analyzed. Among the 906 chronically ill patients, the current smoking prevalence was 26.2%. About 64.3% of smokers with chronic diseases attempted to quit smoking, 21.0% of which successfully quitted. The odds ratio (OR) of smokers with only one chronic disease to quit smoking successfully was higher than that of those who have other diseases (OR = 2.037, 95% confidence interval (CI) = 1.060–3.912; p < 0.05). The smokers who were always restricted to smoking in public places were more likely to quit smoking successfully than those who were free to smoke (OR = 2.188, 95% CI = 1.116–4.291; p < 0.05). This study suggests that health literacy, comorbidity of diseases, and psychological counseling should be considered when developing targeted tobacco prevention strategies. Strengthening tobacco control measures in public places such as rural medical institutions will be effective.
Effects of Responsible Human Resource Management Practices on Female Employees’ Turnover Intentions

This study focuses on the effects of socially responsible human resource management (SR-HRM) practices on female employees’ turnover intentions and the moderating effect of supervisor gender on this relationship. With a sample of 212 female employees from eight different industries in Finland, the results indicate that SR-HRM practices promoting equal career opportunities and work–family integration play a significant role in reducing women's turnover intentions. The study adds to the academic discourse of corporate social responsibility by highlighting the impact of the organizational-level HRM determinants on the individual-level outcome. In addition, supervisor gender makes a difference in the studied relationship: female supervisors have a stronger and more significant impact on the relationship than male supervisors. Our findings suggest that organizational measures which support work–family integration should be taken seriously to decrease female employees’ turnover intentions. Male supervisors could adopt some gender-incongruent leadership behaviors, such as individualized emotional concern and caring when dealing with female employees. In the future, other gender combinations in the supervisor–employee relationship would merit research.
Analysis of a Random Forests Model

Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Despite growing interest and practical use, there has been little exploration of the statistical properties of random forests, and little is known about the mathematical forces driving the algorithm. In this paper, we offer an in-depth analysis of a random forests model suggested by Breiman (2004), which is very close to the original algorithm. We show in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.
HCPP: Cryptography Based Secure EHR System for Patient Privacy and Emergency Healthcare

Privacy concern is arguably the major barrier that hinders the deployment of electronic health record (EHR) systems which are considered more efficient, less error-prone, and of higher availability compared to traditional paper record systems. Patients are unwilling to accept the EHR system unless their protected health information (PHI) containing highly confidential data is guaranteed proper use and disclosure, which cannot be easily achieved without patients' control over their own PHI. However, cautions must be taken to handle emergencies in which the patient may be physically incompetent to retrieve the controlled PHI for emergency treatment. In this paper, we propose a secure EHR system, HCPP (Healthcaresystem for Patient Privacy), based on cryptographic constructions and existing wireless network infrastructures, to provide privacy protection to patients under any circumstances while enabling timelyPHI retrieval for life-saving treatment in emergency situations. Furthermore, our HCPP system restricts PHI access to authorized (not arbitrary) physicians, who can be traced and held accountable if the accessed PHI is found improperly disclosed. Last but not least, HCPP leverages wireless network access to support efficient and private storage/retrieval of PHI, which underlies a secure and feasible EHR system.
Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain

This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization of the Correspondence-LDA model that allows for variable spatial representations to be associated with topics, and increased flexibility in terms of the strength of the correspondence between data types induced by the model. We present three variants of GC-LDA, each of which associates topics with a different spatial representation, and apply them to a corpus of neuroimaging data. In the context of this dataset, each topic corresponds to a functional brain region, where the region's spatial extent is captured by a probability distribution over neural activity, and the region's cognitive function is captured by a probability distribution over linguistic terms. We illustrate the qualitative improvements offered by GC-LDA in terms of the types of topics extracted with alternative spatial representations, as well as the model's ability to incorporate a-priori knowledge from the neuroimaging literature. We furthermore demonstrate that the novel features of GC-LDA improve predictions for missing data.
Molecular hydrogen emission from discs in the η Chamaeleontis cluster

ABSTRACT Disks in the 6 Myr old cluster η Chamaeleontis were searched for emission fromhot H 2 . Around the M3 star ECHA J0843.4-7905we detect circumstellar gas orbitingat ∼2 AU. If the gas is UV-excited, the ro-vibrational line traces a hot gas layersupported by a disk of mass ∼0.03 M ⊙ , similar to the minimum mass solar nebula.Such a gas reservoir at 6 Myr would promote the formation and inwards migration ofgas giant planets.Key words: circumstellar matter, infrared:stars, planets:formation, 1 INTRODUCTIONUnderstanding the longevity of protoplanetary disks aroundstars, and their dust and gas content, is an important stepin understanding the formation of planetary systems. Theexploration of the dust content of disks has been carried outfrom near-infrared to millimetre wavelengths, probing fromhot disks near the stellar surface out to hundreds of AU.The dust mass and temperature can be deduced from thespectral energy distribution, although shortwards of the far-infrared the dust emission is optically thick and so detailedmodels are needed to understand the distribution of mate-rial (Wood et al. 2002). Observations of carbon monoxide indisks are used to estimate the mass of molecular gas, how-ever this is subject to a number of uncertainties. The COmay be photodissociated; it freezes out on grains and is onlya small amount of the total gas. The H
Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?

Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas M than scheduled users K because the users' channels are likely to be near-orthogonal when M/K 10. However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users K* depends on M and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of K* in the large-M regime is derived in closed form, while simulations are used to show what happens at finite M, in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal M/K is less than 10 in many cases of practical relevance. Interestingly, K* depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same K.
Semi-Supervised Learning by Mixed Label Propagation

Recent studies have shown that graph-based approaches are effective for semi-supervised learning. The key idea behind many graph-based approaches is to enforce the consistency between the class assignment of unlabeled examples and the pairwise similarity between examples. One major limitation with most graph-based approaches is that they are unable to explore dissimilarity or negative similarity. This is because the dissimilar relation is not transitive, and therefore is difficult to be propagated. Furthermore, negative similarity could result in unbounded energy functions, which makes most graph-based algorithms unapplicable. In this paper, we propose a new graph-based approach, termed as "mixed label propagation" which is able to effectively explore both similarity and dissimilarity simultaneously. In particular, the new framework determines the assignment of class labels by (1) minimizing the energy function associated with positive similarity, and (2) maximizing the energy function associated with negative similarity. Our empirical study with collaborative filtering shows promising performance of the proposed approach.
Penal Substitutionary Atonement and Concern for Suffering: An Empirical Study

The present study tested a hypothesized model of the degree of belief in penal substitutionary atonement (PSA) predicting a negative association with a sense of responsibility for reducing pain and suffering in the world. Gender complementarian beliefs were hypothesized as a mediator of this association based on theological frameworks within the “New Calvinism” movement connecting PSA, gender views, and the positive spiritual significance of pain and suffering. The sample (N = 225) was comprised of masterslevel students at an Evangelical seminary in the Midwestern United States. Results supported a mediating role for complementarianism in the negative association between PSA and responsibility for reducing pain and suffering in the world. Implications of the findings are discussed in terms of theologies of suffering in connection with social ethics. This study highlights the potential interdisciplinary value of empirical research on specific theological views.
Nantonac collaborative filtering: a model-based approach

A recommender system has to collect users' preference data. To collect such data, rating or scoring methods that use rating scales, such as good-fair-poor or a five-point-scale, have been employed. We replaced such collection methods with a ranking method, in which objects are sorted according to the degree of a user's preference. We developed a technique to convert the rankings to scores based on order statistics theory. This technique successfully improved the accuracy of ranking recommended items. However, we targeted only memory-based recommendation algorithms. To test whether or not the use of ranking methods and our conversion technique are effective for wide variety of recommenders, we apply our conversion technique to model-based algorithms.
A Discrete Evolutionary Model for Chess Players' Ratings

The Elo system for rating chess players, also used in other games and sports, was adopted by the World Chess Federation over four decades ago. Although not without controversy, it is accepted as generally reliable and provides a method for assessing players' strengths and ranking them in official tournaments. It is generally accepted that the distribution of players' rating data is approximately normal but, to date, no stochastic model of how the distribution might have arisen has been proposed. We propose such an evolutionary stochastic model, which models the arrival of players into the rating pool, the games they play against each other, and how the results of these games affect their ratings, in a similar manner to the Elo system. Using a continuous approximation to the discrete model, we derive the distribution for players' ratings at time t as a normal distribution, where the variance increases in time as a logarithmic function of t. We validate the model using published rating data from 2007-2010, showing that the parameters obtained from the data can be recovered through simulations of the stochastic model. The distribution of players' ratings is only approximately normal and has been shown to have a small negative skew. We show how to modify our evolutionary stochastic model to take this skewness into account, and we validate the modified model using the published official rating data.
A Reconsideration of the Self-Compassion Scale’s Total Score: Self-Compassion versus Self-Criticism

The Self-Compassion Scale (SCS) is currently the only self-report instrument to measure self-compassion. The SCS is widely used despite the limited evidence for the scale’s psychometric properties, with validation studies commonly performed in college students. The current study examined the factor structure, reliability, and construct validity of the SCS in a large representative sample from the community. The study was conducted in 1,736 persons, of whom 1,643 were included in the analyses. Besides the SCS, data was collected on positive and negative indicators of psychological functioning, as well as on rumination and neuroticism. Analyses included confirmatory factor analyses (CFA), exploratory factor analyses (EFA), and correlations. CFA showed that the SCS’s proposed six-factor structure could not be replicated. EFA suggested a two-factor solution, formed by the positively and negatively formulated items respectively. Internal consistency was good for the two identified factors. The negative factor (i.e., sum score of the negatively formulated items) correlated moderately to strongly to negative affect, depressive symptoms, perceived stress, as well as to rumination and neuroticism. Compared to this negative factor, the positive factor (i.e., sum score of the positively formulated items) correlated weaker to these indicators, and relatively more strongly to positive affect. Results from this study do not justify the common use of the SCS total score as an overall indicator of self-compassion, and provide support for the idea, as also assumed by others, that it is important to make a distinction between self-compassion and self-criticism.
Dictionary Learning and Tensor Decomposition via the Sum-of-Squares Method

We give a new approach to the dictionary learning (also known as "sparse coding") problem of recovering an unknown n x m matrix A (for m ≥ n) from examples of the form [y = Ax + e,] where x is a random vector in Rm with at most τ m nonzero coordinates, and e is a random noise vector in Rn with bounded magnitude. For the case m=O(n), our algorithm recovers every column of A within arbitrarily good constant accuracy in time mO(log m/log(τ-1)), in particular achieving polynomial time if τ = m-δ for any δ>0, and time mO(log m) if τ is (a sufficiently small) constant. Prior algorithms with comparable assumptions on the distribution required the vector $x$ to be much sparser---at most √n nonzero coordinates---and there were intrinsic barriers preventing these algorithms from applying for denser x. We achieve this by designing an algorithm for noisy tensor decomposition that can recover, under quite general conditions, an approximate rank-one decomposition of a tensor T, given access to a tensor T' that is τ-close to T in the spectral norm (when considered as a matrix). To our knowledge, this is the first algorithm for tensor decomposition that works in the constant spectral-norm noise regime, where there is no guarantee that the local optima of T and T' have similar structures. Our algorithm is based on a novel approach to using and analyzing the Sum of Squares semidefinite programming hierarchy (Parrilo 2000, Lasserre 2001), and it can be viewed as an indication of the utility of this very general and powerful tool for unsupervised learning problems.