NeuroSpeech

NeuroSpeech is a software for modeling pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. Although it was developed to model dysarthric speech signals from Parkinson's patients, its structure allows other computer scientists or developers to include other pathologies and/or measures. Different tasks can be performed: (1) modeling of the signals considering the aforementioned speech dimensions, (2) automatic discrimination of Parkinson's vs. non-Parkinson's, and (3) prediction of the neurological state according to the Unified Parkinson's Disease Rating Scale (UPDRS) score. The prediction of the dysarthria level according to the Frenchay Dysarthria Assessment scale is also provided

Recent Developments in Boron-Phosphorus Ring and Cage Chemistry

The chemical and structural diversity of compounds containing Groups 13/15 element combinations continues to expand at a rapid pace. In this paper we outline some recent results from our groups that illustrate the continued evolution of boronphosphorus ring and cage constructs. 15

Materiality in information environments: Objects, spaces, and bodies in three outpatient hemodialysis facilities

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152032/1/asi24277.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152032/2/asi24277_am.pd

ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning

Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human-annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience

Cell‐ and Gene‐Based Therapeutic Strategies for Periodontal Regenerative Medicine

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142086/1/jper1223.pd

Emerging IT risks: insights from German banking

How do German banks manage the emerging risks stemming from IT innovations such as cyber risk? With a focus on process, roles and responsibilities, field data from ten banks participating in the 2014 ECB stress test were collected by interviewing IT managers, risk managers and external experts. Current procedures for handling emerging risks in German banks were identified from the interviews and analysed, guided by the extant literature. A clear gap was found between enterprise risk management (ERM) as a general approach to risks threatening firms’ objectives and ERM’s neglect of emerging risks, such as those associated with IT innovations. The findings suggest that ERM should be extended towards the collection and sharing of knowledge to allow for an initial understanding and description of emerging risks, as opposed to the traditional ERM approach involving estimates of impact and probability. For example, as cyber risks emerge from an IT innovation, the focus may need to switch towards reducing uncertainty through knowledge acquisition. Since individual managers seldom possess all relevant knowledge of an IT innovation, various stakeholders may need to be involved to exploit their expertise

The Human Operculo-Insular Cortex Is Pain-Preferentially but Not Pain-Exclusively Activated by Trigeminal and Olfactory Stimuli

Increasing evidence about the central nervous representation of pain in the brain suggests that the operculo-insular cortex is a crucial part of the pain matrix. The pain-specificity of a brain region may be tested by administering nociceptive stimuli while controlling for unspecific activations by administering non-nociceptive stimuli. We applied this paradigm to nasal chemosensation, delivering trigeminal or olfactory stimuli, to verify the pain-specificity of the operculo-insular cortex. In detail, brain activations due to intranasal stimulation induced by non-nociceptive olfactory stimuli of hydrogen sulfide (5 ppm) or vanillin (0.8 ppm) were used to mask brain activations due to somatosensory, clearly nociceptive trigeminal stimulations with gaseous carbon dioxide (75% v/v). Functional magnetic resonance (fMRI) images were recorded from 12 healthy volunteers in a 3T head scanner during stimulus administration using an event-related design. We found that significantly more activations following nociceptive than non-nociceptive stimuli were localized bilaterally in two restricted clusters in the brain containing the primary and secondary somatosensory areas and the insular cortices consistent with the operculo-insular cortex. However, these activations completely disappeared when eliminating activations associated with the administration of olfactory stimuli, which were small but measurable. While the present experiments verify that the operculo-insular cortex plays a role in the processing of nociceptive input, they also show that it is not a pain-exclusive brain region and allow, in the experimental context, for the interpretation that the operculo-insular cortex splay a major role in the detection of and responding to salient events, whether or not these events are nociceptive or painful

In situ guided tissue regeneration in musculoskeletal diseases and aging: Implementing pathology into tailored tissue engineering strategies

In situ guided tissue regeneration, also addressed as in situ tissue engineering or endogenous regeneration, has a great potential for population-wide “minimal invasive” applications. During the last two decades, tissue engineering has been developed with remarkable in vitro and preclinical success but still the number of applications in clinical routine is extremely small. Moreover, the vision of population-wide applications of ex vivo tissue engineered constructs based on cells, growth and differentiation factors and scaffolds, must probably be deemed unrealistic for economic and regulation-related issues. Hence, the progress made in this respect will be mostly applicable to a fraction of post-traumatic or post-surgery situations such as big tissue defects due to tumor manifestation. Minimally invasive procedures would probably qualify for a broader application and ideally would only require off the shelf standardized products without cells. Such products should mimic the microenvironment of regenerating tissues and make use of the endogenous tissue regeneration capacities. Functionally, the chemotaxis of regenerative cells, their amplification as a transient amplifying pool and their concerted differentiation and remodeling should be addressed. This is especially important because the main target populations for such applications are the elderly and diseased. The quality of regenerative cells is impaired in such organisms and high levels of inhibitors also interfere with regeneration and healing. In metabolic bone diseases like osteoporosis, it is already known that antagonists for inhibitors such as activin and sclerostin enhance bone formation. Implementing such strategies into applications for in situ guided tissue regeneration should greatly enhance the efficacy of tailored procedures in the future