Quantitative behavioural phenotyping to investigate anaesthesia induced neurobehavioural impairment

Anaesthesia exposure to the developing nervous system causes neuroapoptosis and behavioural impairment in vertebrate models. Mechanistic understanding is limited, and target-based approaches are challenging. High-throughput methods may be an important parallel approach to drug-discovery and mechanistic research. The nematode worm Caenorhabditis elegans is an ideal candidate model. A rich subset of its behaviour can be studied, and hundreds of behavioural features can be quantified, then aggregated to yield a ‘signature’. Perturbation of this behavioural signature may provide a tool that can be used to quantify the effects of anaesthetic regimes, and act as an outcome marker for drug screening and molecular target research. Larval C. elegans were exposed to: isoflurane, ketamine, morphine, dexmedetomidine, and lithium (and combinations). Behaviour was recorded, and videos analysed with automated algorithms to extract behavioural features. Anaesthetic exposure during early development leads to persisting behavioural variation (in total, 125 features across exposure combinations). Higher concentrations, and combinations of isoflurane with ketamine, lead to persistent change in a greater number of features. Morphine and dexmedetomidine do not appear to lead to behavioural impairment. Lithium rescues the neurotoxic phenotype produced by isoflurane. Findings correlate well with vertebrate research: impairment is dependent on agent, is concentration-specific, is more likely with combination therapies, and can potentially be rescued by lithium. These results suggest that C. elegans may be an appropriate model with which to pursue phenotypic screens for drugs that mitigate the neurobehavioural impairment. Some possibilities are suggested for how high-throughput platforms might be organised in service of this field.

Conflict-Driven Satisfiability for Theory Combination: Lemmas, Modules, and Proofs

Search-based satisfiability procedures try to build a model of the input formula by simultaneously proposing candidate models and deriving new formulae implied by the input. Conflict-driven procedures perform non-trivial inferences only when resolving conflicts between formulæ and assignments representing the candidate model. CDSAT (Conflict-Driven SATisfiability) is a method for conflict-driven reasoning in unions of theories. It combines inference systems for individual theories as theory modules within a solver for the union of the theories. This article augments CDSAT with a more general lemma learning capability and with proof generation. Furthermore, theory modules for several theories of practical interest are shown to fulfill the requirements for completeness and termination of CDSAT. Proof generation is accomplished by a proof-carrying version of the CDSAT transition system that produces proof objects in memory accommodating multiple proof formats. Alternatively, one can apply to CDSAT the LCF approach to proofs from interactive theorem proving, by defining a kernel of reasoning primitives that guarantees the correctness by construction of CDSAT proofs.

The Psychoacoustics of Automatic Speech Recognition

Automatic speech recognition (ASR) software has been suggested as a candidate model of the human auditory system thanks to dramatic improvements in performance in recent years. To test this hypothesis, we compared several state-of-the-art ASR systems to results from humans on a barrage of standard psychoacoustic experiments. While some systems showed qualitative agreement with humans in some tests, in others all tested systems diverged markedly from humans. In particular, none of the models used spectral invariance, temporal fine structure or speech periodicity in a similar way to humans. We conclude that none of the tested ASR systems are yet ready to act as a strong proxy for human speech recognition. However, we note that the more recent systems with better performance also tend to better match human results, suggesting that continued cross-fertilisation of ideas between human and automatic speech recognition may be fruitful. Our software is released as an open-source toolbox to allow researchers to assess future ASR systems or add additional psychoacoustic measures.

The CSES global geomagnetic field model (CGGM): an IGRF-type global geomagnetic field model based on data from the China Seismo-Electromagnetic Satellite

Using magnetic field data from the China Seismo-Electromagnetic Satellite (CSES) mission, we derive a global geomagnetic field model, which we call the CSES Global Geomagnetic Field Model (CGGM). This model describes the Earth’s magnetic main field and its linear temporal evolution over the time period between March 2018 and September 2019. As the CSES mission was not originally designed for main field modelling, we carefully assess the ability of the CSES orbits and data to provide relevant data for such a purpose. A number of issues are identified, and an appropriate modelling approach is found to mitigate these. The resulting CGGM model appears to be of high enough quality, and it is next used as a parent model to produce a main field model extrapolated to epoch 2020.0, which was eventually submitted on October 1, 2019 as one of the IGRF-13 2020 candidate models. This CGGM candidate model, the first ever produced by a Chinese-led team, is also the only one relying on a data set completely independent from that used by all other candidate models. A successful validation of this candidate model is performed by comparison with the final (now published) IGRF-13 2020 model and all other candidate models. Comparisons of the secular variation predicted by the CGGM parent model with the final IGRF-13 2020–2025 predictive secular variation also reveal a remarkable agreement. This shows that, despite their current limitations, CSES magnetic data can already be used to produce useful IGRF 2020 and 2020–2025 secular variation candidate models to contribute to the official IGRF-13 2020 and predictive secular variation models for the coming 2020–2025 time period. These very encouraging results show that additional efforts to improve the CSES magnetic data quality could make these data very useful for long-term monitoring of the main field and possibly other magnetic field sources, in complement to the data provided by missions such as the ESA Swarm mission.

Multistage classification of oral histopathological images using improved residual network

<abstract> <p>Oral cancer is a prevalent disease happening in the head and neck region. Due to the high occurrence rate and serious consequences of oral cancer, an accurate diagnosis of malignant oral tumors is a major priority. Thus, early diagnosis is very effective to give the patient a prompt response to treatment. The most efficient way for diagnosing oral cancer is from histopathological imaging, which provides a detailed view of inside cells. Accurate and automatic classification of oral histopathological images remains a difficult task due to the complex nature of cell images, staining methods, and imaging conditions. The use of deep learning in imaging techniques and computational diagnostics can assist doctors and physicians in automatically analysing Oral Squamous Cell Carcinoma biopsy images in a timely and efficient manner. Thus, it reduces the operational workload of the pathologist and enhance patient management. Training deeper neural networks takes considerable time and requires a lot of computing resources, due to the complexity of the network and the gradient diffusion problem. With this motivation and inspired by ResNet's significant successes to handle the gradient diffusion problem, in this study we suggest the novel improved ResNet-based model for the automated multistage classification of oral histopathology images. Three prospective candidate model blocks are presented, analyzed, and the best candidate model is chosen as the optimal one which can efficiently classify the oral lesions into well-differentiated, moderately-differentiated and poorly-differentiated in significantly reduced time, with 97.59% accuracy.</p> </abstract>

Slenderness coefficient models for tree species in Omo biosphere reserve, South-western Nigeria

This study was carried out to aid the prediction of tree slenderness coefficient using non-linear regression models for tree species in Omo Biosphere Reserve, Southwestern Nigeria. Systematic line transect design was adopted for the study. Three transects were laid with four plots on each transect at alternate positions which made a total of 12 sample plots (50 m × 50 m) in the study area. Diameter at breast height (DBH), diameter at the top, diameter at the middle and diameter at the base as well as total height and merchantable height of all trees were measured. Descriptive statistics, Pearson’s correlation and regression analysis were adopted for the study. The study showed that about 23.5% of the trees in the study area are susceptible to wind-throw damage. Correlation analysis revealed that DBH is a better predictor of Slenderness coefficient than other tree growth characteristics. Six non-linear models were adopted for the tree slenderness coefficient prediction. The best models were selected based on the highest Adj.R2, lowest AIC and SEE values. Normal logarithmic equation SLC = 30.72 + (-41.21) In(D) was selected as the candidate model for the pooled data. The same candidate model (Natural logarithm) was selected for both the Desplatsia lutea and Strombosia pustulata species with the equation SLC = -0.04 + (-63.82) In(D) and SLC = 22.12 + (-51.40) In(D) respectively while exponential model with equation SLC = 170.94e(-1.93) was selected for Sterculia rhinopetala. These equations were recommended for predicting slenderness coefficient for each of the tree species in Omo Biosphere with apparently valid potentials for enhancing reasonable quantification of the stands’ stability.

Quantum corrections to the accretion onto a Schwarzschild black hole in the background of quintessence

It is well known that quantum effects may lead to removal of the intrinsic singularity point of back holes. Also, the quintessence scalar field is a candidate model for describing late-time acceleration expansion. Accordingly, Kazakov and Solodukhin considered the existence of back-reaction of the spacetime due to the quantum fluctuations of the background metric to deform a Schwarzschild black hole, which led to a change of the intrinsic singularity of the black hole to a 2-sphere with a radius of the order of the Planck length. Also, Kiselev rewrote the Schwarzschild metric by taking into account the quintessence field in the background. In this study, we consider the quantum-corrected Schwarzschild black hole inspired by Kazakov–Solodukhin’s work, and the Schwarzschild black hole surrounded by quintessence deduced by Kiselev to study the mutual effects of quantum fluctuations and quintessence on the accretion onto the black hole. Consequently, the radial component of the 4-velocity and the proper energy density of the accreting fluid have a finite value on the surface of its central 2-sphere due to the presence of quantum corrections. Also, by comparing the accretion parameters in different kinds of black holes, we infer that the presence of a point-like electric charge in the spacetime is somewhat similar to some quantum fluctuations in the background metric.

Fixed-point tracking of English reading text based on mean shift and multi-feature fusion

English text is difficult to recognize under the interference of blurred background, so it is necessary to improve the fixed-point tracking technology of English text. Based on machine learning algorithms, this paper studies the fixed-point tracking model of English reading text based on mean shift and multi-feature fusion. The target tracking algorithm based on mean shift obtains the description of the target model and the candidate model by calculating the pixel feature probability in the target area and the candidate area. Then, it uses the similarity function to measure the similarity between the initial frame target model and the current candidate model, selects the candidate model that maximizes the similarity function, and obtains the target model mean offset vector. Finally, it continuously iteratively calculates the offset vector based on this vector, and finally converges to the true position of the target, thereby achieving the effect of tracking. In general, it is verified that the model constructed in this paper works well through control experiments.

Machine learning for predicting AF ablation outcomes using daily heart rhythm data at baseline

Background While numerous studies have shown that catheter ablation is superior to antiarrhythmic drug (AAD) in treating atrial fibrillation (AF), the long term outcomes have been limited by arrhythmia recurrence. Reliable data and methods to predict ablation outcomes will thus be valuable for treatment planning. Objective To evaluate the utility of machine learning and various types of input variables, viz. patient characteristics at baseline, and daily heart rhythm data recorded prior to ablation for outcome prediction. Methods We acquired permission to analyze data collected from a randomized clinical trial that recorded daily biomeasures from &gt;345 patients who were referred for first catheter ablation due to AF refractory to at least one AAD. After standardizing the dataset, each patient sample is characterized by a set of daily measures, viz. heart rate variability (HRV) and AF burden (AFB), which is the total minutes in AF per day. We next performed comparative analyses on 19 candidate model variants to evaluate each model's ability in identifying patients who were to experience at least one episode of AF recurrence during post-ablation period starting from day 91 up to day 365 post-ablation, per standard guidelines. We examined: i) use of a set of daily biomeasures jointly with baseline sex and age; and ii) observation lengths of the pre-ablation period. We also examined the use of baseline CHA2DS2-VASc scores, left-atrial volume (LAV), atrial diameter, medical history. We conducted multiple sets of 3-fold cross validation (CV) experiments, each fold independently trained a candidate model with 236 samples (two thirds of the dataset) and performed evaluation on the left-out samples. About 50% of cohort belongs to one class. Each fold scored a model and its input variables in terms of sensitivity (SEN), specificity (SPEC), area under receiver operating characteristic curve (AUC), etc. To circumvent risks of overfitting highly parameterized models to our training subset, we shortlisted 19 models that have few hyper-parameters, e.g. stepwise regression, random forest (RF), linear discriminant analysis (LDA). Results CV results demonstrated that LDA and RF gave comparable performances, with RF achieving highest AUC of 0.68±0.06 using 30 days of rhythm data prior to ablation (SEN of 65.9±7.82; SPEC of 66.3±0.57). When observation period extended to 90 days prior, AUC improved to 0.691±0.02. In contrast, use of LAV alone was not adequate to predict outcome (AUC∼0.5), and when combined with all aforementioned baseline variables, the best model achieved AUC of 0.58±0.05. Feature analyses from the trained models suggest that AFB had highest relevance in predicting outcome. Using only daily AFB, RF and LDA respectively achieved AUC of 0.608±0.04 and 0.652±0.04. Conclusions Our results suggest the value of pre-ablation rhythm data for improving outcome-prediction. Future work will validate these findings using large public datasets. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Huawei-Data Science Institute Research Program; Natural Sciences and Engineering Research Council of Canada (NSERC)