Effect of Cryopreservation Protocol on Post-Thaw Characteristics of Stallion Spermatozoa

Three ejaculates from each of eight stallions were initially centrifuged in INRA 96 extender and spermatozoal pellets were resuspended in a milk/egg yolk-based freezing extender or an egg yolk-based freezing extender. Extended semen was exposed to a fast pre-freeze cooling rate (FAST - semen immediately subjected to cryopreservation) or a slow pre-freeze cooling rate (SLOW - semen pre-cooled at a controlled rate for 80 minutes prior to cryopreservation). After thawing, semen was diluted in initial freezing medium (FM) or INRA 96 prior to analysis of 9 experimental endpoints: total motility (MOT; %), progressive motility (PMOT; %), curvilinear velocity (VCL; um/sec), average-path velocity (VAP; ?m/sec), straight-line velocity (VSL; ?m/sec), linearity (LIN; %), intact acrosomal and plasma membranes (AIVIAB; %), intact acrosomal membranes (AI; %), and intact plasma membranes (VIAB; %). Eight of nine experimental endpoints (MOT, PMOT, VAP, VSL, LIN AIVIAB, AI, and VIAB) were affected by extender type, with LE extender yielding higher values than MF extender for these variables (P<0.05). Exposure of extended semen to a slow pre-freeze cooling period resulted in increased values for seven of nine endpoints, as compared to a fast pre-freeze cooling period (P less than 0.05). Mean VAP and VSL were unaffected by prefreeze cooling rate (P>0.05). As a post-thaw diluent, INRA 96 yielded higher mean values than FM for MOT, PMOT, VCL, VAP, and VSL (P less than 0.05). Treatment group FM yielded slightly higher values than INRA 96 for LIN and VIAB (P less than 0.05). Extender x rate interactions (P less than 0.05) were detected for the variables MOT, AIVIAB, AI and VIAB. Mean values for these endpoints were higher following spermatozoal exposure to a slow pre-freeze cooling period, regardless of freezing extender type (P less than 0.05). The effects of pre-freeze cooling rate on MOT, AIVIAB, AI, and VIAB were more pronounced in spermatozoa cryopreserved in MF extender, as compared to LE extender. Within treatment groups SLOW and FAST, mean MOT, AIVIAB, AI, and VIAB were higher (P less than 0.05) for spermatozoa cryopreserved in LE extender, as compared to MF extender. Extender x diluent interactions (P less than 0.05) were detected for MOT, PMOT, VCL, VAP, VSL, and LIN. Within Group MF, mean MOT, PMOT, VCL, VAP, and VSL were higher in INRA diluent, as compared to FM diluent (P less than 0.05). Within Group LE, FM diluent yielded slightly higher values than INRA diluent for PMOT, VAP, VSL, and LIN (P less than 0.05). In conclusion, a slow pre-freeze cooling rate was superior to a fast pre-freeze cooling rate, regardless of freezing extender used, and INRA 96 served as a satisfactory post-thaw diluent prior to semen analysis

On the Restriction of the Optimal Transportation Problem to the set of Martingale Measures with Uniform Marginals

One of the fundamental problems in mathematical finance is the pricing of derivative assets such as op- tions. In practice, pricing an exotic option, whose value depends on the price evolution of an underlying risky asset, requires a model and then numerical simulations. Having no a priori model for the risky asset, but only the knowledge of its distribution at certain times, we instead look for a lower bound for the option price using the Monge-Kantorovich transportation theory. In this paper, we consider the Monge-Kantorovich problem that is restricted over the set of martingale measure. In order to solve such problem, we first look at sufficient conditions for the existence of an optimal martingale measure. Next, we focus our attention on problems with transports which are two-dimensional real martingale measures with uniform marginals. We then come up with some characterization of the optimizer, using measure-quantization approach

Accuracy of Fitbit Activity Trackers During Walking in a Controlled Setting

Activity trackers are widely used to measure daily physical activity. Many devices have been shown to measure steps more accurately at higher intensities, however, it is also important to determine the accuracy of these new devices at measuring steps while walking at a pace similar to that used during most daily activities. PURPOSE: To assess the accuracy of 6 popular activity trackers at measuring steps while walking on a treadmill. METHODS: Twenty-six college students (Mean±SD; 22.1±3.7yrs; 25.1±4.0kg/m2; 13 male) walked 500 steps at 3mph on a treadmill while wearing 6 different activity trackers (Pedometer, Fitbit Blaze, Charge HR, Alta, Flex, Zip, One). The Charge HR was placed two fingers above the right wrist while the Flex was next to the wrist bone. The Blaze was placed two fingers above the left wrist while the Alta was next to the wrist bone. The Fitbit Zip and the One were aligned with the hipbone on the left and right waistband respectively. Steps were counted by a trained researcher using a hand tally counter. Missing values were replaced with the mean value for that device. Step counts were correlated between Fitbit devices and the pedometer and tally counter using Pearson correlations. Significance was set at p\u3c0.05. Mean bias scores were calculated between the step counts for each device and the tally counter. Mean Absolute Percent Error (MAPE) values were also calculated for each device relative to the tally counter. RESULTS: Fitbit Zip and One were significantly correlated with the tally counter (r=0.50, p\u3c0.05; r=0.68, p\u3c0.01, respectively) while the other devices were not significantly correlated. Mean bias and MAPE values were as follows: Device (Mean Bias/MAPE) Pedometer (-0.2±39.2/3.8±6.8), Blaze (34.5±67.1/9.9±11.3), Charge HR (-12.6±61.5/7.0±10.3), Alta (-85.0±70.8/17.1±14.1), Flex (49.5±242.4/19.7±45.3), Zip (1.8±3.4/0.4±0.6), One (0.2±2.1/0.3±0.3). Fitbit Zip and One were within one half percent of actual steps while wrist-worn Fitbits ranged from 7.0-19.7% from actual step counts. CONCLUSION: Consistent with previous research, activity trackers worn at the waist provide the most accurate step counts compared to wrist-worn models. Differences found in wrist-worn models may result in significant over- or underestimation of activity levels when worn for long periods of time

Comparison of Smartphone Pedometer Apps on a Treadmill versus Outdoors

Previous research has focused on the accuracy of smartphone pedometer apps in laboratory settings, however less information is available in outdoor (free living) environments. PURPOSE: Determine the accuracy of 5 smartphone apps at recording steps at a walking speed in a laboratory versus an outdoor setting. METHODS: Twenty-three healthy college students consented (Mean±SD; 22±3.8yrs; BMI 24.9±4.13kg/m2) to participate in 2 separate visits. During the first visit participants walked 500 steps at 3mph on a treadmill while wearing a pedometer and a smartphone placed in the pocket using 5 pedometer apps concurrently (Moves, Google Fit (G-Fit), Runtastic, Accupedo, S-Health). During the second visit, participants walked 400 meters at 3mph on a sidewalk outside. Actual steps for each visit were recorded using a hand tally counter device. Zero and negative values were replaced with the mean value for that trial. Statistical analyses were performed using IBM SPSS 23.0. Mean bias scores were calculated between the step count for each app and the respective tally count for each trial. Mean bias scores were correlated between trials for each app using Pearson correlations and significance was set at p\u3c0.05. Mean Absolute Percent Error (MAPE) values were also calculated for each app for both trials. RESULTS: G-Fit recorded 2 zero values and 2 negative values and Moves recorded 1 zero value. Mean bias scores were significantly correlated between the indoor and outdoor protocols for the pedometer (r=0.67, p\u3c0.01) and S-Health (r=0.46, p\u3c0.5). The remaining apps were not correlated between protocols. The outdoor protocol producing a greater mean bias for the outdoor protocol for G-Fit, Runtastic, and Accupedo (mean bias ± SD indoor, outdoor; -4.3±53.1, -19.3±120.0; -10.7±63.3, -33.4±118.7; 16.0±143.6, 79.0±75.0; respectively) and a greater mean bias for the indoor protocol for the pedometer, Moves, and S-Health (mean bias indoor, outdoor; -1.4±41.5, 0.0±34.1; -117.4±196.7, -42.2±209.6; 11.3±28.4, 0.0±58.7; respectively). MAPE was below 5% for the pedometer and S-Health for both trials. CONCLUSION: Apps with the lowest error in a controlled setting may be less affected when used in other settings, while apps with greater variation in a controlled setting may be affected when used in a different environment

CrimeNet: Neural Structured Learning using Vision Transformer for violence detection

The state of the art in violence detection in videos has improved in recent years thanks to deep learning models, but it is still below 90% of average precision in the most complex datasets, which may pose a problem of frequent false alarms in video surveillance environments and may cause security guards to disable the artificial intelligence system. In this study, we propose a new neural network based on Vision Transformer (ViT) and Neural Structured Learning (NSL) with adversarial training. This network, called CrimeNet, outperforms previous works by a large margin and reduces practically to zero the false positives. Our tests on the four most challenging violence-related datasets (binary and multi-class) show the effectiveness of CrimeNet, improving the state of the art from 9.4 to 22.17 percentage points in ROC AUC depending on the dataset. In addition, we present a generalisation study on our model by training and testing it on different datasets. The obtained results show that CrimeNet improves over competing methods with a gain of between 12.39 and 25.22 percentage points, showing remarkable robustness.MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR ” HORUS project - Grant n. PID2021-126359OB-I0

Validity of Daily Physical Activity Measurements of Fitbit Charge 2

Physical activity monitors collect continuous data to provide a summary of daily activity. The Fitbit Charge 2 monitors heart rate as well as steps, calories, and active minutes throughout the day. There is currently no research validating the Fitbit Charge 2 at measuring daily physical activity levels in a real life setting. PURPOSE: To compare measures of daily steps and active minutes of Fitbit Charge 2 with a research-grade accelerometer. METHODS: Sixteen active college students (Mean±SD; 23±4.9yrs; 16.43±10.19%fat; 9 male) consented to be part of the study. Participants wore an ActiGraph GT3X accelerometer and Fitbit Charge 2 concurrently for seven consecutive days. Both devices were programed with each participant’s information and the participants were instructed to perform their daily activities wearing both devices and only remove them to shower and to sleep. Data were considered valid when participants wore both devices for at least 10 hours on 4 or more days of the week. Steps and active minutes (moderate-vigorous physical activity) were recorded by each device. Mean bias was calculated by subtracting ActiGraph steps and active minutes from those obtained from the Fitbit Charge 2 for each day and an average daily mean bias was calculated using values from all seven days. Absolute percentage error was also calculated [100(|Fitbit Charge 2 - ActiGraph|)/ActiGraph] to indicate the overall 7-day difference between the Fitbit Charge 2 and ActiGraph. Pearson correlations and paired sample t-test were performed to compare Fitbit Charge 2 measurements with the corresponding ActiGraph measurements with significance considered at p\u3c0.05. RESULTS: The Fitbit Charge 2 overestimated steps by 2,451.3±2085.4 compared to the ActiGraph using the daily average steps over the seven days. This was 32.2±40.7% above the ActiGraph measurement. Average mean bias for daily active minutes was -52.1±58.9 with the Fitbit Charge 2 underestimating compared to the ActiGraph. Active minutes for the Fitbit Charge 2 were an average of 69±26.1% away from the ActiGraph. Steps for the Fitbit Charge 2 were significantly correlated to ActiGraph steps (r=0.575, p=0.02) while active minutes were not significantly correlated (r= -0.255, p=0.34). Paired sample t-test results showed a significant difference between the Fitbit Charge 2 steps and active minutes compared with the ActiGraph (p\u3c0.01 for both). CONCLUSION: The Fitbit Charge 2 may be useful for measuring steps in a free-living environment, however active minutes are significantly underestimated

Predicting tyrosinaemia: a mathematical model of 4-hydroxyphenylpyruvate dioxygenase inhibition by nitisinone in rats

Nitisinone or 2-(2-nitro-4-trifluoromethylbenzoyl)cyclohexane-1,3-dione, is a reversible inhibitor of 4- hydroxyphenylpyruvate dioxygenase (HPPD), an enzyme important in tyrosine catabolism. Today, nitisinone is successfully used to treat Hereditary Tyrosinaemia type 1, although its original expected role was as a herbicide. In laboratory animals, treatment with nitisinone leads to the elevation of plasma tyrosine (tyrosinaemia). In rats and Beagle dogs, repeat low-dose exposure to nitisinone leads to corneal opacities whilst similar studies in the mouse and Rhesus monkey showed no comparable toxicities or other treatment related findings. The differences in toxicological sensitivities have been related to the upper limit of the concentration of tyrosine that accumulates in plasma, which is driven by the amount/activity of tyrosine aminotransferase. A physiologically based, pharmacodynamics ordinary differential equation model of HPPD inhibition to bolus exposure of nitisinone in vivo is presented. Going beyond traditional approaches, asymptotic analysis is used to separate the different timescales of events involved in HPPD inhibition and tyrosinaemia. This analysis elucidates, in terms of the model parameters, a critical inhibitor concentration (at which tyrosine concentration starts to rise) and highlights the contribution of in vitro measured parameters to events in an in vivo system. Furthermore, using parameter-fitting methods, a systematically derived reduced model is shown to fit well to rat data, making explicit how the parameters are informed by such data. This model in combination with in vitro descriptors has potential as a surrogate for animal experimentation to predict tyrosinaemia, and further development can extend its application to other related medical scenarios