Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson's Disease Detection and Speech Features Extraction.

Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.

Communicating computational workflows in a regulatory environment.

The volume of nucleic acid sequence data has exploded recently, amplifying the challenge of transforming data into meaningful information. Processing data can require an increasingly complex ecosystem of customized tools, which increases difficulty in communicating analyses in an understandable way yet is of sufficient detail to enable informed decisions or repeats. This can be of particular interest to institutions and companies communicating computations in a regulatory environment. BioCompute Objects (BCOs; an instance of pipeline documentation that conforms to the IEEE 2791-2020 standard) were developed as a standardized mechanism for analysis reporting. A suite of BCOs is presented, representing interconnected elements of a computation modeled after those that might be found in a regulatory submission but are shared publicly - in this case a pipeline designed to identify viral contaminants in biological manufacturing, such as for vaccines.

Mutation Maker, An Open Source Oligo Design Platform for Protein Engineering.

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic, and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multisite random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.

The timed up & go test sit-to-stand transition: Which signals measured by inertial sensors are a viable route for continuous analysis?

BACKGROUND: The Timed Up and Go test is a well-known clinical test for assessing of mobility and fall risk. It has been shown that the IMU which use an accelerometer and gyroscope are capable of analysing the quantitative parameters of the sit-to-stand transition. RESEARCH QUESTION: Which signals obtained by the inertial sensors are suitable for continuous Timed Up & Go test sit-to-stand transition analysis? METHODS: In the study we included 29 older adult volunteers and 31 de-novo Parkinson disease (PD) patients. All subjects performed an instrumented extended TUG wearing a gyro-accelerometer. The sit-to-stand transition was detected from an angular velocity signal. The sit-to-stand signal pattern within the subject group was analyzed via an intra-class correlation between curves. Inter-subjects' variability was visualized using prediction bands. RESULTS: The angular velocity about the pitch axis exhibited the best signal match across subjects in both groups (0.50 < ICC < 0.75). When analysing acceleration, the acceleration along the antero-posterior axis showed moderate inter-subjects signal pattern match (0.50 < ICC < 0.75) in the reference group. The analysis of other signals revealed a poor signal pattern in both subject groups. SIGNIFICANCE: For optimal interpretation of the analysis of continuous curves, the signal pattern must be considered. Also, the inter-subject variability along this pattern can be informative and useful.

The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability.

Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.

Instrumental analysis of finger tapping reveals a novel early biomarker of parkinsonism in idiopathic rapid eye movement sleep behaviour disorder.

BACKGROUND: Idiopathic rapid eye movement sleep behaviour (iRBD) is considered as a risk factor for Parkinson's disease (PD) development. Evaluation of repetitive movements with finger tapping, which serves as a principal task to measure the extent of bradykinesia in PD, may undercover potential PD patients. The aim of this study was to explore whether finger tapping abnormalities, evaluated with a 3D motion capture system, are already present in RBD patients. METHODS: Finger tapping data was acquired using a contactless 3D motion capture system from 40 RBD subjects and compared to 25 de-novo PD patients and 25 healthy controls. Objective assessment of amplitude decrement, maximum opening velocity and their combination representing finger tapping decrement was performed in the sequence of the first ten tapping movements. The association between instrumental finger tapping data and semi-quantitative clinical evaluation was analyzed. RESULTS: While significant differences between PD and controls were found for all investigated finger tapping measures (p < 0.002), RBD differed from controls in finger tapping amplitude (p = 0.004) and velocity (p = 0.007) decrement but not in maximal opening velocity. A significant relationship between the motor score from the Movement Disorders Society - Unified Parkinson's Disease Rating Scale and finger tapping decrement was shown for both patient groups, ie RBD (r = 0.36, p = 0.02) and PD (r = 0.60, p = 0.002). CONCLUSIONS: In our group of RBD patients we demonstrated amplitude decrement of repetitive movements, which may correspond with prodromal bradykinesia. Our findings suggest instrumental analysis of finger tapping abnormalities as a potential novel clinical marker reflecting subclinical motor disturbances in RBD.

A deep learning genome-mining strategy for biosynthetic gene cluster prediction.

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.

Reshaping cortical activity with subthalamic stimulation in Parkinson's disease during finger tapping and gait mapped by near infrared spectroscopy.

Exploration of motor cortex activity is essential to understanding the pathophysiology in Parkinson's Disease (PD), but only simple motor tasks can be investigated using a fMRI or PET. We aim to investigate the cortical activity of PD patients during a complex motor task (gait) to verify the impact of deep brain stimulation in the subthalamic nucleus (DBS-STN) by using Near-Infrared-Spectroscopy (NIRS). NIRS is a neuroimaging method of brain cortical activity using low-energy optical radiation to detect local changes in (de)oxyhemoglobin concentration. We used a multichannel portable NIRS during finger tapping (FT) and gait. To determine the signal activity, our methodology consisted of a pre-processing phase for the raw signal, followed by statistical analysis based on a general linear model. Processed recordings from 9 patients were statistically compared between the on and off states of DBS-STN. DBS-STN led to an increased activity in the contralateral motor cortex areas during FT. During gait, we observed a concentration of activity towards the cortex central area in the "stimulation-on" state. Our study shows how NIRS can be used to detect functional changes in the cortex of patients with PD with DBS-STN and indicates its future use for applications unsuited for PET and a fMRI.