Close approximations to the sound of a cochlear implant.

Cochlear implant (CI) systems differ in terms of electrode design and signal processing. It is likely that patients fit with different implant systems will experience different percepts when presented speech via their implant. The sound quality of speech can be evaluated by asking single-sided-deaf (SSD) listeners fit with a cochlear implant (CI) to modify clean signals presented to their typically hearing ear to match the sound quality of signals presented to their CI ear. In this paper, we describe very close matches to CI sound quality, i.e., similarity ratings of 9.5 to 10 on a 10-point scale, by ten patients fit with a 28 mm electrode array and MED EL signal processing. The modifications required to make close approximations to CI sound quality fell into two groups: One consisted of a restricted frequency bandwidth and spectral smearing while a second was characterized by a wide bandwidth and no spectral smearing. Both sets of modifications were different from those found for patients with shorter electrode arrays who chose upshifts in voice pitch and formant frequencies to match CI sound quality. The data from matching-based metrics of CI sound quality document that speech sound-quality differs for patients fit with different CIs and among patients fit with the same CI.

Pilot study on the effectiveness of the socialmind program for the rehabilitation of social cognition following acquired brain injury.

Background: People with acquired brain injury (ABI) often have Social Cognition (SC) deficits. Impairment of SC causes the individual to have difficulties in daily functioning and can lead to social isolation. Research aimed at rehabilitation of SC in individuals with ABI is scarce and almost always addresses only one component of this ability. Objective: This pilot study aimed to assess the effectiveness of the new "SocialMind" program in improving all core components of SC in people with ABI. Method: The study included 31 participants with ABI, divided into experimental and control groups. The study spanned 44 weeks, involving an initial meeting, evaluation, training, and final assessment phases. The SocialMind program, structured into four modules, each with a duration of 30 h, targeted each SC component through tailored exercises. The program addressed emotion recognition, social awareness, ToM, and empathy. Results: The SocialMind group demonstrated significant improvements in emotion recognition (p = 0.017), social knowledge (p < 0.001), and empathy (p = 0.001) compared to the control group. ToM also showed a notable improvement that approached significance (p = 0.057). Conclusion: This pilot study suggests that the SocialMind program effectively enhances three of the four core components of SC in individuals with ABI.

Alpha and theta oscillations on a visual strategic processing task in age-related hearing loss.

Introduction: Emerging evidence suggests changes in several cognitive control processes in individuals with age-related hearing loss (ARHL). However, value-directed strategic processing, which involves selectively processing salient information based on high value, has been relatively unexplored in ARHL. Our previous work has shown behavioral changes in strategic processing in individuals with ARHL. The current study examined event-related alpha and theta oscillations linked to a visual, value-directed strategic processing task in 19 individuals with mild untreated ARHL and 17 normal hearing controls of comparable age and education. Methods: Five unique word lists were presented where words were assigned high- or low-value based on the letter case, and electroencephalography (EEG) data was recorded during task performance. Results: The main effect of the group was observed in early time periods. Specifically, greater theta synchronization was seen in the ARHL group relative to the control group. Interaction between group and value was observed at later time points, with greater theta synchronization for high- versus low-value information in those with ARHL. Discussion: Our findings provide evidence for oscillatory changes tied to a visual task of value-directed strategic processing in individuals with mild untreated ARHL. This points towards modality-independent neurophysiological changes in cognitive control in individuals with mild degrees of ARHL and adds to the rapidly growing literature on the cognitive consequences of ARHL.

Classification of Patients' Judgments of Their Physicians in Web-Based Written Reviews Using Natural Language Processing: Algorithm Development and Validation.

BACKGROUND: Patients increasingly rely on web-based physician reviews to choose a physician and share their experiences. However, the unstructured text of these written reviews presents a challenge for researchers seeking to make inferences about patients' judgments. Methods previously used to identify patient judgments within reviews, such as hand-coding and dictionary-based approaches, have posed limitations to sample size and classification accuracy. Advanced natural language processing methods can help overcome these limitations and promote further analysis of physician reviews on these popular platforms. OBJECTIVE: This study aims to train, test, and validate an advanced natural language processing algorithm for classifying the presence and valence of 2 dimensions of patient judgments in web-based physician reviews: interpersonal manner and technical competence. METHODS: We sampled 345,053 reviews for 167,150 physicians across the United States from Healthgrades.com, a commercial web-based physician rating and review website. We hand-coded 2000 written reviews and used those reviews to train and test a transformer classification algorithm called the Robustly Optimized BERT (Bidirectional Encoder Representations from Transformers) Pretraining Approach (RoBERTa). The 2 fine-tuned models coded the reviews for the presence and positive or negative valence of patients' interpersonal manner or technical competence judgments of their physicians. We evaluated the performance of the 2 models against 200 hand-coded reviews and validated the models using the full sample of 345,053 RoBERTa-coded reviews. RESULTS: The interpersonal manner model was 90% accurate with precision of 0.89, recall of 0.90, and weighted F1-score of 0.89. The technical competence model was 90% accurate with precision of 0.91, recall of 0.90, and weighted F1-score of 0.90. Positive-valence judgments were associated with higher review star ratings whereas negative-valence judgments were associated with lower star ratings. Analysis of the data by review rating and physician gender corresponded with findings in prior literature. CONCLUSIONS: Our 2 classification models coded interpersonal manner and technical competence judgments with high precision, recall, and accuracy. These models were validated using review star ratings and results from previous research. RoBERTa can accurately classify unstructured, web-based review text at scale. Future work could explore the use of this algorithm with other textual data, such as social media posts and electronic health records.

An atypical form of 60S ribosomal subunit in Diamond-Blackfan anemia linked to RPL17 variants.

Diamond-Blackfan anemia syndrome (DBA) is a ribosomopathy associated with loss-of-function variants in more than 20 ribosomal protein (RP) genes. Here, we report the genetic, functional and biochemical dissection of two multigenerational pedigrees with variants in RPL17, a large ribosomal subunit protein-encoding gene. Affected individuals had clinical features and erythroid proliferation defects consistent with DBA. Furthermore, RPL17/uL22 depletion resulted in anemia and micrognathia in zebrafish larvae, and in vivo complementation studies indicated that RPL17 variants were pathogenic. Lymphoblastoid cell lines (LCLs) derived from patients displayed a ribosomal RNA maturation defect reflecting haploinsufficiency of RPL17. The proteins encoded by RPL17 variants were not incorporated into ribosomes, but 10-20% of 60S ribosomal subunits contained a short form of 5.8S rRNA (5.8SC), a species that is marginal in normal cells. These atypical 60S subunits were actively engaged in translation. Ribosome profiling showed changes of the translational profile, but those are similar to LCLs bearing RPS19 variants. These results link an additional RP gene to DBA. They show that ribosomes can be modified substantially by RPL17 haploinsufficiency, but support the paradigm that translation alterations in DBA are primarily related to insufficient ribosome production rather than to changes in ribosome structure or composition.

Quantification of two derivatives of malic acid first-time discovered in coffee: Influence of postharvest processing method.

This study quantified, for the first time, 2-isopropylmalic and 3-isopropylmalic acids, in green, roasted and espresso coffee by UHPLC-MS/MS. Moreover, it reports the influence of postharvest processing methods (natural, washed and honey) on their content. New extraction techniques were developed and validated from three coffee matrices (green, roasted and espresso). Honey coffee exhibited levels substantially higher of 2-isopropylmalic acid than those processed by natural and washed methods (p < 0.05). Specifically, 2-isopropylmalic acid levels in honey green, roasted and espresso coffee samples were 48.24 ± 7.31 ng/g, 168.8 ± 10.88 ng/g and 177.5 ± 9.49 ng/g, respectively. This research highlights the significant impact of processing methods on the chemical profile of coffee and introduces 2-isopropylmalic and 3-isopropylmalic acids as potential quality indicators. Moreover, it suggests that the fermentation stage during processing may play a crucial role in their formation, laying the foundation for optimizing coffee processing to enhance quality.

Leonhard Med, a trusted research environment for processing sensitive research data.

This paper provides an overview of the development and operation of the Leonhard Med Trusted Research Environment (TRE) at ETH Zurich. Leonhard Med gives scientific researchers the ability to securely work on sensitive research data. We give an overview of the user perspective, the legal framework for processing sensitive data, design history, current status, and operations. Leonhard Med is an efficient, highly secure Trusted Research Environment for data processing, hosted at ETH Zurich and operated by the Scientific IT Services (SIS) of ETH. It provides a full stack of security controls that allow researchers to store, access, manage, and process sensitive data according to Swiss legislation and ETH Zurich Data Protection policies. In addition, Leonhard Med fulfills the BioMedIT Information Security Policies and is compatible with international data protection laws and therefore can be utilized within the scope of national and international collaboration research projects. Initially designed as a "bare-metal" High-Performance Computing (HPC) platform to achieve maximum performance, Leonhard Med was later re-designed as a virtualized, private cloud platform to offer more flexibility to its customers. Sensitive data can be analyzed in secure, segregated spaces called tenants. Technical and Organizational Measures (TOMs) are in place to assure the confidentiality, integrity, and availability of sensitive data. At the same time, Leonhard Med ensures broad access to cutting-edge research software, especially for the analysis of human -omics data and other personalized health applications.

Antioxidant and antimicrobial activities of Myrtus communis essential oils in cold-stored lactic butter.

BACKGROUND: The use of natural antioxidants and antimicrobials in dairy production can increase the variety of dairy-based products. In this study, the antioxidant and antimicrobial changes in lactic butter samples made from heat-treated creams and enriched with M. communis essential oils (EOs) were investigated. RESULTS: The best lactic butter properties were achieved by optimizing the process at 70 and 80 °C. M. communis EOs decreased lipid oxidation and spoilage microorganism growth in lactic butter during cold storage. M. communis EOs have antioxidant and antimicrobial activity in lactic butter equal to that of ascorbyl palmitate. α-Pinene, p-cymene, limonene, 3-carene, 1,8-cineol, ß-linalool, α-terpineol and myretenol are the major contributors to the antioxidant and antimicrobial activities of M. communis EOs. They exhibit antioxidant activity by neutralizing free radicals by donating hydrogen or acting as termination enhancers, and antimicrobial activity by disruption of cell membranes, which may result in the leakage of macromolecules or the loss of essential metabolites, ultimately leading to cell death during the storage of lactic butter samples. CONCLUSION: The addition of M. communis EOs improves lactic butter stability equal to that of ascorbyl palmitate, and may be applied as a natural and effective preservative to maintain butter from lipid oxidation and microbial spoilage and enhance its safety. PRACTICAL APPLICATIONS: The growing recognition of the health benefits of natural antioxidants, as opposed to synthetic ones, has led to the development of new applications for natural antioxidants. In this regard, M. communis L. EOs can be used to enhance the shelf stability of cold-stored lactic butter. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Cognitive deficits and course of recovery in transient global amnesia: a systematic review.

OBJECTIVE: Published evidence suggests that cognitive impairment during a TGA (transient global amnesia) spell may not be confined to episodic memory. We undertook a systematic review to determine the pattern of cognitive deficits during a TGA episode. As a secondary objective, we aimed to delineate the course of cognitive recovery. METHODS: MEDLINE, EMBASE, CENTRAL, and Google scholar were systematically searched up to October 2023. Observational controlled studies including 10 or more TGA patients (Hodges and Warlow criteria) were retrieved. Data from case-control, cross-sectional, and cohort studies were reviewed and qualitatively synthesized. RESULTS: Literature search yielded 1302 articles. After the screening of titles and abstracts, 115 full texts were retrieved and 17 of them were included in the present systematic review. During the acute phase, spatiotemporal disorientation, dense anterograde and variable retrograde amnesia, semantic memory retrieval difficulties, and working memory deficits comprised the neuropsychological profile of patients with TGA. Visuospatial abilities, attention and psychomotor speed, semantic memory, confrontation naming, and other measures of executive function (apart from semantic fluency and working memory) were consistently found normal. In the course of recovery, after the resolution of repetitive questioning, the restoration of spatiotemporal orientation follows, working memory and semantic memory retrieval ensue, while episodic memory impairment persists for longer. Meticulous evaluations may reveal subtle residual memory (especially recognition) deficits even after 24 h. CONCLUSIONS: Μemory impairment, spatiotemporal disorientation, and working memory deficits constitute the pattern of cognitive impairment during a TGA spell. Residual memory deficits may persist even after 24 h.

An automated and high-throughput data processing workflow for PFAS identification in biota by direct infusion ultra-high resolution mass spectrometry.

The increasing recognition of the health impacts from human exposure to per- and polyfluorinated alkyl substances (PFAS) has surged the need for sophisticated analytical techniques and advanced data analyses, especially for assessing exposure by food of animal origin. Despite the existence of nearly 15,000 PFAS listed in the CompTox chemicals dashboard by the US Environmental Protection Agency, conventional monitoring and suspect screening methods often fall short, covering only a fraction of these substances. This study introduces an innovative automated data processing workflow, named PFlow, for identifying PFAS in environmental samples using direct infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). PFlow's validation on a bream liver sample, representative of low-concentration biota, involves data pre-processing, annotation of PFAS based on their precursor masses, and verification through isotopologues. Notably, PFlow annotated 17 PFAS absent in the comprehensive targeted approach and tentatively identified an additional 53 compounds, thereby demonstrating its efficiency in enhancing PFAS detection coverage. From an initial dataset of 30,332 distinct m/z values, PFlow thoroughly narrowed down the candidates to 84 potential PFAS compounds, utilizing precise mass measurements and chemical logic criteria, underscoring its potential in advancing our understanding of PFAS prevalence and of human exposure.

EXPRESS: Detection and Visualization of Minor Irradiation-Induced Changes in Polytetrafluoroethylene (PTFE) Using Raman Spectroscopy.

Exposure of polytetrafluoroethylene to argon plasma results in chemical modification of the polymer near the surface. Interestingly, PTFE modification can be induced by the sub-band gap ultraviolet UV irradiation. In the latter case, the changes in the chemical structure are very subtle, and they are practically invisible to conventional experimental techniques. Raman spectra of irradiated and raw samples show practically identical peaks. However, the baseline that is commonly considered as an unwanted spectral component, contains an important information that reflects the minor structural changes. With the proper data analysis, this allows us to visualize the effects of the argon plasma and sub-band gap UV irradiation on the polymer.

FAST-EM array tomography: a workflow for multibeam volume electron microscopy.

Elucidating the 3D nanoscale structure of tissues and cells is essential for understanding the complexity of biological processes. Electron microscopy (EM) offers the resolution needed for reliable interpretation, but the limited throughput of electron microscopes has hindered its ability to effectively image large volumes. We report a workflow for volume EM with FAST-EM, a novel multibeam scanning transmission electron microscope that speeds up acquisition by scanning the sample in parallel with 64 electron beams. FAST-EM makes use of optical detection to separate the signals of the individual beams. The acquisition and 3D reconstruction of ultrastructural data from multiple biological samples is demonstrated. The results show that the workflow is capable of producing large reconstructed volumes with high resolution and contrast to address biological research questions within feasible acquisition time frames.

Evaluating large language models for health-related text classification tasks with public social media data.

OBJECTIVES: Large language models (LLMs) have demonstrated remarkable success in natural language processing (NLP) tasks. This study aimed to evaluate their performances on social media-based health-related text classification tasks. MATERIALS AND METHODS: We benchmarked 1 Support Vector Machine (SVM), 3 supervised pretrained language models (PLMs), and 2 LLMs-based classifiers across 6 text classification tasks. We developed 3 approaches for leveraging LLMs: employing LLMs as zero-shot classifiers, using LLMs as data annotators, and utilizing LLMs with few-shot examples for data augmentation. RESULTS: Across all tasks, the mean (SD) F1 score differences for RoBERTa, BERTweet, and SocBERT trained on human-annotated data were 0.24 (±0.10), 0.25 (±0.11), and 0.23 (±0.11), respectively, compared to those trained on the data annotated using GPT3.5, and were 0.16 (±0.07), 0.16 (±0.08), and 0.14 (±0.08) using GPT4, respectively. The GPT3.5 and GPT4 zero-shot classifiers outperformed SVMs in a single task and in 5 out of 6 tasks, respectively. When leveraging LLMs for data augmentation, the RoBERTa models trained on GPT4-augmented data demonstrated superior or comparable performance compared to those trained on human-annotated data alone. DISCUSSION: The results revealed that using LLM-annotated data only for training supervised classification models was ineffective. However, employing the LLM as a zero-shot classifier exhibited the potential to outperform traditional SVM models and achieved a higher recall than the advanced transformer-based model RoBERTa. Additionally, our results indicated that utilizing GPT3.5 for data augmentation could potentially harm model performance. In contrast, data augmentation with GPT4 demonstrated improved model performances, showcasing the potential of LLMs in reducing the need for extensive training data. CONCLUSIONS: By leveraging the data augmentation strategy, we can harness the power of LLMs to develop smaller, more effective domain-specific NLP models. Using LLM-annotated data without human guidance for training lightweight supervised classification models is an ineffective strategy. However, LLM, as a zero-shot classifier, shows promise in excluding false negatives and potentially reducing the human effort required for data annotation.

The role of general cognitive skills in integrating visual and linguistic information during sentence comprehension: individual differences across the lifespan.

Individuals exhibit massive variability in general cognitive skills that affect language processing. This variability is partly developmental. Here, we recruited a large sample of participants (N = 487), ranging from 9 to 90 years of age, and examined the involvement of nonverbal processing speed (assessed using visual and auditory reaction time tasks) and working memory (assessed using forward and backward Digit Span tasks) in a visual world task. Participants saw two objects on the screen and heard a sentence that referred to one of them. In half of the sentences, the target object could be predicted based on verb-selectional restrictions. We observed evidence for anticipatory processing on predictable compared to non-predictable trials. Visual and auditory processing speed had main effects on sentence comprehension and facilitated predictive processing, as evidenced by an interaction. We observed only weak evidence for the involvement of working memory in predictive sentence comprehension. Age had a nonlinear main effect (younger adults responded faster than children and older adults), but it did not differentially modulate predictive and non-predictive processing, nor did it modulate the involvement of processing speed and working memory. Our results contribute to delineating the cognitive skills that are involved in language-vision interactions.

A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations.

Effective communication hinges on a mutual understanding of word meaning in different contexts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We developed a model-based coupling framework that aligns brain activity in both speaker and listener to a shared embedding space from a large language model (LLM). The context-sensitive LLM embeddings allow us to track the exchange of linguistic information, word by word, from one brain to another in natural conversations. Linguistic content emerges in the speaker's brain before word articulation and rapidly re-emerges in the listener's brain after word articulation. The contextual embeddings better capture word-by-word neural alignment between speaker and listener than syntactic and articulatory models. Our findings indicate that the contextual embeddings learned by LLMs can serve as an explicit numerical model of the shared, context-rich meaning space humans use to communicate their thoughts to one another.

A real-time interactive restoration system for intraoral digital videos using segment anything model.

Objective: Poor conditions in the intraoral environment often lead to low-quality photos and videos, hindering further clinical diagnosis. To restore these digital records, this study proposes a real-time interactive restoration system using segment anything model. Methods: Intraoral digital videos, obtained from the vident-lab dataset through an intraoral camera, serve as the input for interactive restoration system. The initial phase employs an interactive segmentation module leveraging segment anything model. Subsequently, a real-time intraframe restoration module and a video enhancement module were designed. A series of ablation studies were systematically conducted to illustrate the superior design of interactive restoration system. Our quantitative evaluation criteria contain restoration quality, segmentation accuracy, and processing speed. Furthermore, the clinical applicability of the processed videos was evaluated by experts. Results: Extensive experiments demonstrated its performance on segmentation with a mean intersection-over-union of 0.977. On video restoration, it leads to reliable performances with peak signal-to-noise ratio of 37.09 and structural similarity index measure of 0.961, respectively. More visualization results are shown on the https://yogurtsam.github.io/iveproject page. Conclusion: Interactive restoration system demonstrates its potential to serve patients and dentists with reliable and controllable intraoral video restoration.

Effects of industrial-scale pickling processes on the dynamic changes in the physicochemical indicators and microbial diversities of Yacai.

The effects of dry-salted and salt-fermented processing on the physicochemical characteristics and microbial communities of Yacai were systematically investigated. The results showed that the contents of total acid, amino acid nitrogen (AAN) and nitrite in the final products of dry-salted Yacai were greater than those in salt-fermented Yacai. Lactic acid was the dominant organic acid in the two types of Yacai. Dry-salted processing is more conducive to forming a high-quality reddish-brown color. During whole pickling process, the microbial diversity of dry-salted Yacai was higher than that of salt-fermented Yacai, particularly in the early and middle stages of fermentation. For dry-salted Yacai, 8 bacteria (Natribacillus, Chromohalobacter, Marinococcus, Lentibacillus, Nesterenkonia, Gracilibacillus, Oceanobacillus and Tetragenococcus) and 1 fungus (Zygosaccharomyces) showed a significant positive correlation with AAN. For salt-fermented Yacai, 8 bacteria (Gracilibacillus, Alkalibacillus, Oceanobacillus, Virgibacillus, Lentibacillus, Salibacterium, Chromohalobacter and Tetragenococcus) and 3 fungi (Zygosaccharomyces, Millerozyma, and Wickerhamomyces) exhibited significant positive correlations with AAN.

Automated pipeline processing X-ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRA III.

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia-water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

Bacterial-fungicidal vine disease detection with proximal aerial images.

Vine disease detection is considered one of the most crucial components in precision viticulture. It serves as an input for several further modules, including mapping, automatic treatment, and spraying devices. In the last few years, several approaches have been proposed for detecting vine disease based on indoor laboratory conditions or large-scale satellite images integrated with machine learning tools. However, these methods have several limitations, including laboratory-specific conditions or limited visibility into plant-related diseases. To overcome these limitations, this work proposes a low-altitude drone flight approach through which a comprehensive dataset about various vine diseases from a large-scale European dataset is generated. The dataset contains typical diseases such as downy mildew or black rot affecting the large variety of grapes including Muscat of Hamburg, Alphonse Lavallée, Grasa de Cotnari, Rkatsiteli, Napoca, Pinot blanc, Pinot gris, Chambourcin, Feteasca regala, Sauvignon blanc, Muscat Ottonel, Merlot, and Seyve-Villard 18402. The dataset contains 10,000 images and more than 100,000 annotated leaves, verified by viticulture specialists. Grape bunches are also annotated for yield estimation. Further, tests were made against state-of-the-art detection methods on this dataset, focusing also on viable solutions on embedded devices, including Android-based phones or Nvidia Jetson boards with GPU. The datasets, as well as the customized embedded models, are available on the project webpage.