Objective Acoustic analysis of voice has the potential to expedite detection and diagnosis of voice disorders. Applying an image‐based, neural‐network approach to analyzing the acoustic signal may be an effective means for detecting and differentially diagnosing voice disorders. The purpose of this study is to provide a proof‐of‐concept that embedded data within human phonation can be accurately and efficiently decoded with deep learning neural network analysis to differentiate between normal and disordered voices. Methods Acoustic recordings from 10 vocally‐healthy speakers, as well as 70 patients with one of seven voice disorders (n = 10 per diagnosis), were acquired from a clinical database. Acoustic signals were converted into spectrograms and used to train a convolutional neural network developed with the Keras library. The network architecture was trained separately for each of the seven diagnostic categories. Binary classification tasks (ie, to classify normal vs. disordered) were performed for each of the seven diagnostic categories. All models were validated using the 10‐fold cross‐validation technique. Results Binary classification averaged accuracies ranged from 58% to 90%. Models were most accurate in their classification of adductor spasmodic dysphonia, unilateral vocal fold paralysis, vocal fold polyp, polypoid corditis, and recurrent respiratory papillomatosis. Despite a small sample size, these findings are consistent with previously published data utilizing deep neural networks for classification of voice disorders. Conclusion Promising preliminary results support further study of deep neural networks for clinical detection and diagnosis of human voice disorders. Current models should be optimized with a larger sample size. Levels of Evidence Level III

Abstract. Kazakhstan is naturally rich in fossil fuels and its economy is strongly linked to oil and gas exports. Significant coal reserves have led to an energy mix that is dominated by aging and polluting thermal power plants. Yet Kazakhstan comprises mainly grassland steppe where agriculture and livestock pastoralism dominate offering the potential for cleaner, renewable energy production from a range of agricultural and forestry wastes. Here we analyse the spatial distribution and bioenergy generation potential of different feedstocks using an ArcGIS platform and demonstrate a significant opportunity for a range of bioenergy technologies. We recommend a number of policy interventions to enable Kazakhstan to make a transition to cleaner, more accessible and locally generated supply which is also sustainable and provide a waste management solution.

Background Triple-negative breast cancer (TNBC) is a heterogeneous disease and we have previously shown that rapid relapse of TNBC is associated with distinct sociodemographic features. We hypothesized that rapid versus late relapse in TNBC is also defined by distinct clinical and genomic features of primary tumors. Methods Using three publicly-available datasets, we identified 453 patients diagnosed with primary TNBC with adequate follow-up to be characterized as ‘rapid relapse’ (rrTNBC; distant relapse or death ≤2 years of diagnosis), ‘late relapse’ (lrTNBC; > 2 years) or ‘no relapse’ (nrTNBC: > 5 years no relapse/death). We explored basic clinical and primary tumor multi-omic data, including whole transcriptome (n = 453), and whole genome copy number and mutation data for 171 cancer-related genes (n = 317). Association of rapid relapse with clinical and genomic features were assessed using Pearson chi-squared tests, t-tests, ANOVA, and Fisher exact tests. We evaluated logistic regression models of clinical features with subtype versus two models that integrated significant genomic features. Results Relative to nrTNBC, both rrTNBC and lrTNBC had significantly lower immune signatures and immune signatures were highly correlated to anti-tumor CD8 T-cell, M1 macrophage, and gamma-delta T-cell CIBERSORT inferred immune subsets. Intriguingly, lrTNBCs were enriched for luminal signatures. There was no difference in tumor mutation burden or percent genome altered across groups. Logistic regression mModels that incorporate genomic features significantly outperformed standard clinical/subtype models in training (n = 63 patients), testing (n = 63) and independent validation (n = 34) cohorts, although performance of all models were overall modest. Conclusions We identify clinical and genomic features associated with rapid relapse TNBC for further study of this aggressive TNBC subset.

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Purpose: Metastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death <2 years) reflect unique genomic features relative to late relapse (lrTNBC; >2 years). Patients and Methods:We identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or 'no relapse' (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures. Results:Patients with rrTNBC were higher stage at diagnosis (Chi-square p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (Chi-square p=0.03). Among 125 expression signatures, five immune signatures were significantly higher in nrTNBCs while lrTNBC were enriched for eight estrogen/luminal signatures (all FDR p<0.05). There was no significant difference in tumor mutation burden or percent genome altered across the groups. Among mutations, only TP53 mutations were significantly more frequent in rrTNBC compared to lrTNBC (Fisher exact FDR p=0.009). To develop an optimal classifier, we used 77 significant clinical and 'omic features to evaluate six modeling approaches encompassing simple, machine learning, and artificial neural network (ANN). Support vector machine outperformed other models with average receiver-operator characteristic area under curve >0.75. Conclusions:We provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.

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<div class="section abstract"><div class="htmlview paragraph">To decarbonize heavy-duty vehicles solely through electrification with batteries is challenging as large batteries are required for a meaningful range, severely impacting payload. Employment of hybrid electric powertrains where fuel cells are integrated with batteries can deliver increased range and payload. However, the energy balance between the fuel cell and the battery needs to be analyzed to optimize the sizing of the powertrain components. This study has performed a multi-objective optimization using genetic algorithm to obtain the optimum range and hydrogen consumption for a DAF 44 tons heavy-duty truck. The proposed truck powertrain has been numerically modelled in AVL CRUISE M software. The electric drive from Involution Technologies Ltd and Bramble Energy Ltd’s printed circuit board fuel cell (PCBFC) are used in the model. The model considers the main powertrain control system variables, and the optimization is performed using AVL real road driving cycle, which is based on high altitude climb for a truck with average power requirement of the motors of 300 kW. From the results of the optimization, five design points were recommended in pareto domain, and the transient results were plotted for these operating points to decide the optimum scenario. At the selected design point the size of the H2 storage tank, fuel cell and battery packs equals to 65 kg H2 on-board storage, 270 kW fuel cell and 257 kWh respectively. The proposed fuel cell truck running the highly demanding AVL drive cycle has a 570 km range, compared with 211 km range for battery-electric only (with battery total capacity of 516.16 kWh) and hydrogen consumption of 12.46 kg/100km at fully laden payload (44 tons).</div></div>