Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of puffed corn-fish snack extrusion conditions using response surface methodology

To optimize the extrusion conditions of novelty developed puffed corn-fish snack, Response Surface Methodology was performed. Feed rate (810-1206 g/min), temperature (105-125oC) and screw speed (146- 208 rpm) were considered as 3 independent variables. Expansion ratio, density, sensory texture, overall acceptability, linear distance and count peak were used as dependent variables. Density and expansion ratio were significantly (p 0.94). Optimum condition was found at 116oC, feed rate at1107 g/min and screw speed at 148 rpm, where the expansion ratio of 5.5 and density of 41.92 kg/m3 were obtained. Besides, the effect of feed rate (0.5-1.5 kg/min) on textural properties was individually studied. A quadratic model (R2 >0.95) was found for expansion ratio versus feed rate while density showed a cubic regression model (R2 >0.80) verses feed rate.
General type-2 fuzzy edge detector applied on face recognition system using neural networks

Edge detection is an essential method used in the image processing systems and can be applied to image sets before the training phase in pattern recognition systems. An edge detector simplifies the analysis of the images; because, it reduces the dataset processed. In this paper we present the advantage to use a fuzzy edge detector method in a face recognition system. In the methodology, first the General type-2 fuzzy edge detector was applied over three image databases; secondly the recognition system was performed using monolithic neural network, and after that the mean recognition rate was obtained; finally the recognition rate is compared using different edge detectors, such as the Sobel operator, Type-1 and Interval Type-2 fuzzy edge detectors.
Predictors of Time to Union After Operative Fixation of Closed Ankle Fractures

Background. Ankle fractures are common and represent a significant burden to society. We aim to report the rate of union as determined by clinical and radiographic data, and to identify factors that predict time to union. Methods. A cohort of 112 consecutive patients with isolated, closed, operative malleolar ankle fractures treated with open reduction and internal fixation was retrospectively reviewed for time to clinical union. Clinical union was defined based on radiographic and clinical parameters, and delayed union was defined by time to union >12 weeks. Injury characteristics, patient factors and treatment variables were recorded, and statistical techniques employed included the Chi-square test, the Student’s T-test, and multivariate linear regression modeling. Results. Forty-two (37.5%) of patients who achieved union did so in less than 12 weeks, and 69 (61.6%) of these patients demonstrated delayed union at a mean of 16.7 weeks (range, 12.1-26.7 weeks), and the remaining patient required revision surgery. Factors associated with higher rates of delayed union or increased time to union included tobacco use, bimalleolar fixation, and high energy mechanism (all p<0.05). In regression analysis, statistically significant negative predictors of time to union were BMI, dislocation of the tibiotalar joint, external fixation for initial stabilization and delay of definitive management (all p<0.05). Conclusion. Patient characteristics, injury factors and treatment variables are predictive of time to union following open reduction and internal fixation of closed ankle fractures. These findings should assist with patient counseling, and help guide the provider when considering adjunctive therapies that promote bone healing. Levels of Evidence: Prognostic, Level IV: Case series
Replication for Efficiency and Fault Tolerance in a Dsm System

Distributed Shared Memory (DSM) systems implemented on a network of workstations (NOW) have become a convenient alternative to shared memory archi-tectures to execute long running parallel applications. However, such architectures are susceptible to experience failures. This paper presents the design and implementation of a recoverable DSM (RDSM) based on a backward error recovery (BER) mechanism. Our RDSM's design has focused on exploiting replication of data for both fault-tolerance and eeciency. This RDSM has been implemented on a NOW and performance evaluation shows the beneets of exploiting both types of replication to design an eecient, scalable and low-cost recoverable DSM.
The Validity of the Uriscreen Test for Early Detection of Urinary Tract Infection in Children

Objective. To determine the validity of the Uriscreen, a rapid diagnostic test based on the detection of urine catalase for the early detection of urinary tract infection (UTI) in children, compared with standard urinalysis and dipstick tests. Study Design. Cross-sectional study. Study Population. Children 1 month to 17 years of age who presented to the emergency department of a pediatric tertiary care center between March and November of 1996 with symptoms suggestive of UTI. Methods. Urine specimens obtained from a random sample of 121 patients were evaluated simultaneously for possible UTI by Uriscreen (catalase test), urinalysis (microscopic pyuria), dipstick (leukocyte esterase and nitrite), and quantitative urine culture. All specimens were collected by one of three sterile techniques (midstream void technique, bladder catheterization, or suprapubic aspiration), as appropriate for age, and tested immediately. Using the quantitative urine culture as the gold standard (reference test), the sensitivity, specificity, and positive and negative predictive values of all the screening tests were determined and compared. Age, sex, temperature, presenting symptoms, and method of urine collection were recorded for each participant. Results. Of the 121 patients, 35 (28.9%) had positive culture results: 30 girls (85.7%) and 5 boys (14.3%). Compared with urinalysis and dipstick tests, Uriscreen had the highest sensitivity (100% vs 88.6% and 97.1%, respectively) and the highest negative predictive value (100% vs 95% and 98.6%, respectively), but the poorest specificity (68.6% vs 88.4% and 82.5%, respectively) and positive predictive value (56.4% vs 75.6% and 69.4%, respectively). Conclusions. The clinical use of Uriscreen for the presumptive diagnosis of UTI in children is limited and not significantly superior to urinalysis or the dipstick test. However, because of its 100% sensitivity and negative predictive value and its ease of use, rapidity, and low cost, it is recommended highly for ruling out the diagnosis of UTI. In laboratories, a negative Uriscreen result may prevent the need for performing expensive urine cultures.
Community Health Volunteers in Primary Healthcare in Rural Uganda: Factors Influencing Performance

Introduction Community health volunteers (CHVs) play an integral role in primary healthcare. Several countries rely on CHV programs as a major element in improving access to care and attaining universal health coverage. However, their performance has been heterogeneous and at times context-specific, and influenced by multiple factors. We describe the socio-demographic and workplace characteristics affecting CHVs’ performance in a public health program in rural western Uganda. Methods This was a cross-sectional study based on routine program data of CHVs serving the catchment of Bwindi Community Hospital, Kanungu District, South Western Uganda, in 2014 and 2015. Information was collected on individual socio-demographic and workplace characteristics of the CHVs. To assess their work output, we defined study-specific targets in terms of attendance at monthly CHVs’ meetings with community health nurses, households followed-up and reported, children screened for malnutrition, immunization coverage, and health facility deliveries. Frequencies and proportions are reported for characteristics and outputs and odds ratios for study-specific factors associated with overall performance. Results Of the 508 CHVs, 65% were women, 48% were aged 35 years and below, and 37% took care of more than the recommended 20–30 households. Seventy-eight percent of the CHVs had ≥80% of pregnant women under their care delivering in health units, 71% had ≥95% of the children on schedule for routine immunization, while 27% screened ≥75% of the children under 5 years for malnutrition. More refresher trainings was associated with better overall performance [adjusted odds ratio (aOR): 12.2, 95% confidence interval (CI): 1.6–93.6, P = 0.02] while overseeing more than the recommended 20–30 households reduced overall performance (aOR: 0.6, 95% CI: 0.4–0.9, P = 0.02). Conclusion Being in-charge of more than the recommended households was associated with reduced performance of CHVs, while more refresher trainings were associated with improved performance. If the CHVs are to remain a strategic pillar in universal health coverage, it is imperative to address those factors known to impact on their performance.
Interactions of Burkholderia pseudomallei and Acanthamoeba castellanii and Their Effects on Virulence in Human Monocytes

Interactions of Burkholderia pseudomallei and Acanthamoeba castellanii and their Effects on Virulence in Human Monocytes Emily A. Moore Department of Microbiology and Molecular Biology Master of Science Burkholderia pseudomallei, the etiological agent of melioidosis, is a saprophytic bacterium existing endemically in the water and soil of SE Asia and Northern Australia. This organism has shown the ability to remain dormant in its host for decades. B. thailandensis is a closely related non-pathogenic near neighbor that is also found in these soils. It has been suggested that free-living amoeba could be natural reservoirs for these organisms. The interactions of Burkholderia species and Acanthamoeba castellanii, a species of free-living amoeba, were studied to better understand the natural ecology of these organisms and to determine the effects amoeba interactions might have on pathogenesis. In this study, the adherence and persistence of several B. pseudomallei clinical isolates were compared to that of B. thailandensis within both amoeba and a human monocyte cell line. Results showed that B. pseudomallei isolates can enter amoeba and survive therein at varying levels of efficiency. Some isolates were able to persist inside the amoeba for up to three weeks. Optimal entry time into an amoeba trophozoite was found to be about three hours for all ten B. pseudomallei isolates. Interestingly, it was found that after internalization by amoeba, B. pseudomallei have a significantly increased ability to both attach to, and grow within human monocytes, suggesting that such interactions increase the virulence capabilities of soil isolates.
Visual odometry learning for unmanned aerial vehicles

This paper addresses the problem of using visual information to estimate vehicle motion (a.k.a. visual odometry) from a machine learning perspective. The vast majority of current visual odometry algorithms are heavily based on geometry, using a calibrated camera model to recover relative translation (up to scale) and rotation by tracking image features over time. Our method eliminates the need for a parametric model by jointly learning how image structure and vehicle dynamics affect camera motion. This is achieved with a Gaussian Process extension, called Coupled GP, which is trained in a supervised manner to infer the underlying function mapping optical flow to relative translation and rotation. Matched image features parameters are used as inputs and linear and angular velocities are the outputs in our non-linear multi-task regression problem. We show here that it is possible, using a single uncalibrated camera and establishing a first-order temporal dependency between frames, to jointly estimate not only a full 6 DoF motion (along with a full covariance matrix) but also relative scale, a non-trivial problem in monocular configurations. Experiments were performed with imagery collected with an unmanned aerial vehicle (UAV) flying over a deserted area at speeds of 100–120 km/h and altitudes of 80-100 m, a scenario that constitutes a challenge for traditional visual odometry estimators.
RAD51 Mutants Cause Replication Defects and Chromosomal Instability

ABSTRACT RAD51 is important for restarting stalled replication forks and for repairing DNA double-strand breaks (DSBs) through a pathway called homology-directed repair (HDR). However, analysis of the consequences of specific RAD51 mutants has been difficult since they are toxic. Here we report on the dominant effects of two human RAD51 mutants defective for ATP binding (K133A) or ATP hydrolysis (K133R) expressed in mouse embryonic stem (ES) cells that also expressed normal mouse RAD51 from the other chromosome. These cells were defective for restarting stalled replication forks and repairing breaks. They were also hypersensitive to camptothecin, a genotoxin that generates breaks specifically at the replication fork. In addition, these cells exhibited a wide range of structural chromosomal changes that included multiple breakpoints within the same chromosome. Thus, ATP binding and hydrolysis are essential for chromosomal maintenance. Fusion of RAD51 to a fluorescent tag (enhanced green fluorescent protein [eGFP]) allowed visualization of these proteins at sites of replication and repair. We found very low levels of mutant protein present at these sites compared to normal protein, suggesting that low levels of mutant protein were sufficient for disruption of RAD51 activity and generation of chromosomal rearrangements.
Systems Biology of Tomato Fruit Development: Combined Transcript, Protein, and Metabolite Analysis of Tomato Transcription Factor (nor, rin) and Ethylene Receptor (Nr) Mutants Reveals Novel Regulatory Interactions1[W][OA]

Tomato (Solanum lycopersicum) is an established model to study fleshy fruit development and ripening. Tomato ripening is regulated independently and cooperatively by ethylene and transcription factors, including nonripening (NOR) and ripening-inhibitor (RIN). Mutations of NOR, RIN, and the ethylene receptor Never-ripe (Nr), which block ethylene perception and inhibit ripening, have proven to be great tools for advancing our understanding of the developmental programs regulating ripening. In this study, we present systems analysis of nor, rin, and Nr at the transcriptomic, proteomic, and metabolomic levels during development and ripening. Metabolic profiling marked shifts in the abundance of metabolites of primary metabolism, which lead to decreases in metabolic activity during ripening. When combined with transcriptomic and proteomic data, several aspects of the regulation of metabolism during ripening were revealed. First, correlations between the expression levels of a transcript and the abundance of its corresponding protein were infrequently observed during early ripening, suggesting that posttranscriptional regulatory mechanisms play an important role in these stages; however, this correlation was much greater in later stages. Second, we observed very strong correlation between ripening-associated transcripts and specific metabolite groups, such as organic acids, sugars, and cell wall-related metabolites, underlining the importance of these metabolic pathways during fruit ripening. These results further revealed multiple ethylene-associated events during tomato ripening, providing new insights into the molecular biology of ethylene-mediated ripening regulatory networks.
ADC characterization of region‐specific response to cerebral perfusion deficit in rats by MRI at 9.4 T

Region‐specific cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) of water in the rat brain were quantified in vivo by high‐field MRI (9.4 T) for 6–7 h after middle cerebral artery occlusion (MCAO). Upon occlusion, average CBF fell from about 1.5–2 ml/g/min to below 0.5 ml/g/min in cortical areas and the amygdala, and below 0.2 ml/g/min in the caudate putamen. CBF in some of the homologous contralateral areas also decreased by 20–30%. Average ADC decreased from about 8 · 10−4 to 5 · 10−4 mm2/s in the caudate putamen and parietal cortex. Corresponding changes in ADC were lower in the frontal cortex and negligible in the piriform cortex, suggesting that the perfusion threshold for ADC decrease may be different for different brain regions in the same animal. The area of decreased ADC correlated well with the infarction area revealed by 2,3,5‐triphenyltetrazolium chloride (TTC) staining of brain slices in vitro. A better understanding of the mechanisms linking ADC and CBF changes to ischemic cell disorders may prove useful in characterizing the degree of tissue damage, and in developing and evaluating treatment strategies. Magn Reson Med 47:562–570, 2002. © 2002 Wiley‐Liss, Inc.
A psychophysical model of the power assist system for lifting objects

In this paper, we designed a 1DOF power assist system for lifting objects. Human's vertical lifting force, comprises of inertial force and gravitational force, was considered as the desired dynamics of the system. We hypothesized that human's perception of object weight due to inertial force might be different from the perceived weight due to gravitational force for lifting object with the power assist system. The hypothesis meant that the mass parameter of the inertial force might be different from the mass parameter of the gravitational force. We then designed the control system of the power assist system based on the desired dynamics and simulated the system using MATLAB. We then psychophysically determined the optimum mass parameters for the inertial and the gravitational force components of the desired dynamics of the power assist system on the basis of human's perception of object weight. The results showed that human's weight perceptual consideration with the dynamics (control system) of the power assist system enhanced maneuverability, stability, naturalness, ease of use, safety etc. when lifting objects with the system. Finally, we suggested using the findings to design human-friendly power assist systems for manipulating heavy objects in various industries.
Signal Decimation and Interpolation in Fractional Domain using Non-linear Basis Functions

This paper presents signal decimation and interpolation techniques under a multiresolution frame work for both lower and higher dimensional applications. New classes of non-linear basis functions have been derived from the sigmoid activation function extensively used in artificial neural networks (ANN). It has been shown that the proposed non-linear basis functions are well suited for interpolation/approximation of band limited signals. An efficient scheme for band limited signal interpolation has been introduced. Fast IIR digital filters (inverse filters) have been derived from the combinatorial theory in connection with the proposed basis functions. The proposed inverse filters can easily be implemented recursively with three multiplications and additions only. Further, the factorization of higher order filters for easy implementation has also been considered. Frequency response characteristics for the pre-filters and their corresponding interpolators are presented to reveal the quality of interpolation. An experiment has been carried out to interpolate a discrete sequence of length 33 into a sequence of length 257 (with a zooming factor of 8). Second part of the paper presents another efficient scheme for image decimation and interpolation. Experimental results on image data compression have been presented to justify the use of the proposed technique.
What do we need to build explainable AI systems for the medical domain?

Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. The central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles, they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.
Maximum agreement subtree in a set of evolutionary trees-metrics and efficient algorithms

In this paper we prove that the maximum homeomorphic agreement subtree problem is /spl Nscr//spl Pscr/-complete for three trees with unbounded degrees. We then show an approximation algorithm of time O(kn/sup 5/) for choosing the species that are not in a maximum agreement subtree of a set of k trees. Our approximation is guaranteed to provide a set that is no more than 4 times the optimum solution. While the set of evolutionary trees may be large in practice, the trees usually have very small degrees, typically no larger than three. We develop a new method for finding a maximum agreement subtree of k trees, of which one has degree bounded by d. This new method enables us to find a maximum agreement subtree in time O(kn/sup d+1/). <<ETX>>
Odds Ratios and Logistic Regression: Further Examples of their use and Interpretation

Logistic regression is perhaps the most widely used method for adjustment of confounding in epidemiologic studies. Its popularity is understandable. The method can simultaneously adjust for confounders measured on different scales; it provides estimates that are clinically interpretable; and its estimates are valid in a variety of study designs with few underlying assumptions. To those of us in practice settings, several aspects of applying and interpreting the model, however, can be confusing and counterintuitive. We attempt to clarify some of these points through several examples. We apply the method to a study of risk factors associated with periventricular leucomalacia and intraventricular hemorrhage in neonates. We relate the logit model to Cornfield's 2 x 2 table and discuss its application to both cohort and case–control study design. Interpretations of odds ratios, relative risk, and β0 from the logit model are presented.