Optimizing the PROTREC network-based missing protein prediction algorithm.

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Revolutionizing smartphone gyrocardiography for heart rate monitoring: overcoming clinical validation hurdles.

Accurate heart rate (HR) measurement is crucial for optimal cardiac health, and while conventional methods such as electrocardiography and photoplethysmography are widely used for continuous daily monitoring, they may face practical limitations due to their dependence on external sensors and susceptibility to motion artifacts. In recent years, mechanocardiography (MCG)-based technologies, such as gyrocardiography (GCG) and seismocardiography (SCG), have emerged as promising alternatives to address these limitations. GCG has shown enhanced sensitivity and accuracy for HR detection compared to SCG, although its benefits are often overlooked in the context of the widespread use of accelerometers in HR monitoring applications. In this perspective, we aim to explore the potential and challenges of GCG, while recognizing that other technologies, including photoplethysmography and remote photoplethysmography, also have promising applications for HR monitoring. We propose a roadmap for future research to unlock the transformative capabilities of GCG for everyday heart rate monitoring.

Detection of heart rate using smartphone gyroscope data: a scoping review.

Heart rate (HR) is closely related to heart rhythm patterns, and its irregularity can imply serious health problems. Therefore, HR is used in the diagnosis of many health conditions. Traditionally, HR has been measured through an electrocardiograph (ECG), which is subject to several practical limitations when applied in everyday settings. In recent years, the emergence of smartphones and microelectromechanical systems has allowed innovative solutions for conveniently measuring HR, such as smartphone ECG, smartphone photoplethysmography (PPG), and seismocardiography (SCG). However, these measurements generally rely on external sensor hardware or are highly susceptible to inaccuracies due to the presence of significant levels of motion artifact. Data from gyrocardiography (GCG), however, while largely overlooked for this application, has the potential to overcome the limitations of other forms of measurements. For this scoping review, we performed a literature search on HR measurement using smartphone gyroscope data. In this review, from among the 114 articles that we identified, we include seven relevant articles from the last decade (December 2012 to January 2023) for further analysis of their respective methods for data collection, signal pre-processing, and HR estimation. The seven selected articles' sample sizes varied from 11 to 435 participants. Two articles used a sample size of less than 40, and three articles used a sample size of 300 or more. We provide elaborations about the algorithms used in the studies and discuss the advantages and disadvantages of these methods. Across the articles, we noticed an inconsistency in the algorithms used and a lack of established standardization for performance evaluation for HR estimation using smartphone GCG data. Among the seven articles included, five did not perform any performance evaluation, while the other two used different reference signals (HR and PPG respectively) and metrics for accuracy evaluation. We conclude the review with a discussion of challenges and future directions for the application of GCG technology.

Deep Learning for Neuroimaging Segmentation with a Novel Data Augmentation Strategy.

Brain insults such as cerebral ischemia and intracranial hemorrhage are critical stroke conditions with high mortality rates. Currently, medical image analysis for critical stroke conditions is still largely done manually, which is time-consuming and labor-intensive. While deep learning algorithms are increasingly being applied in medical image analysis, the performance of these methods still needs substantial improvement before they can be widely used in the clinical setting. Among other challenges, the lack of sufficient labelled data is one of the key problems that has limited the progress of deep learning methods in this domain. To mitigate this bottleneck, we propose an integrated method that includes a data augmentation framework using a conditional Generative Adversarial Network (cGAN) which is followed by a supervised segmentation with a Convolutional Neural Network (CNN). The adopted cGAN generates meaningful brain images from specially altered lesion masks as a form of data augmentation to supplement the training dataset, while the CNN incorporates depth-wise-convolution based X-blocks as well as Feature Similarity Module (FSM) to ease and aid the training process, resulting in better lesion segmentation. We evaluate the proposed deep learning strategy on the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset and show that this approach outperforms the current state-of-art methods in task of stroke lesion segmentation.

Obligate mutualism within a host drives the extreme specialization of a fig wasp genome.

BACKGROUND: Fig pollinating wasps form obligate symbioses with their fig hosts. This mutualism arose approximately 75 million years ago. Unlike many other intimate symbioses, which involve vertical transmission of symbionts to host offspring, female fig wasps fly great distances to transfer horizontally between hosts. In contrast, male wasps are wingless and cannot disperse. Symbionts that keep intimate contact with their hosts often show genome reduction, but it is not clear if the wide dispersal of female fig wasps will counteract this general tendency. We sequenced the genome of the fig wasp Ceratosolen solmsi to address this question. RESULTS: The genome size of the fig wasp C. solmsi is typical of insects, but has undergone dramatic reductions of gene families involved in environmental sensing and detoxification. The streamlined chemosensory ability reflects the overwhelming importance of females finding trees of their only host species, Ficus hispida, during their fleeting adult lives. Despite long-distance dispersal, little need exists for detoxification or environmental protection because fig wasps spend nearly all of their lives inside a largely benign host. Analyses of transcriptomes in females and males at four key life stages reveal that the extreme anatomical sexual dimorphism of fig wasps may result from a strong bias in sex-differential gene expression. CONCLUSIONS: Our comparison of the C. solmsi genome with other insects provides new insights into the evolution of obligate mutualism. The draft genome of the fig wasp, and transcriptomic comparisons between both sexes at four different life stages, provide insights into the molecular basis for the extreme anatomical sexual dimorphism of this species.

Molecular approaches identify known species, reveal cryptic species and verify host specificity of Chinese Philotrypesis (Hymenoptera: Pteromalidae).

Philotrypesis, a major component of the fig wasp community (Hymenoptera: Pteromalidae), is a model taxon for studying male fighting and mating behaviour. Its extreme sexual dimorphism and male polymorphism render species identification uncertain and in-depth research on its ecology, behaviour and other evolutionary topics challenging. The fig wasps' enclosed habitat within the syconia makes their mating behaviour inaccessible, to the extent of matching conspecific females and males. In this study, we combine morphological and molecular analyses to identify species of Philotrypesis sampled from south China and to associate their extraordinarily dimorphic genders and labile male morphologies. Morphological evaluations of females identify 22 species and 28 male morphs. The mitochondrial cytochrome c oxidase I and nuclear internal transcribed spacer 2 data detect 21 species using females, and 15 species among the males. Most of the males match the species as delimited by females. Both markers reveal cryptic species in P. quadrisetosa on Ficus vasculosa. Most species of wasps live on one species of fig but three species co-occur in two hosts (F. microcarpa and F. benjamina), which indicates host switching.

Suppressed TGF-beta1 expression is correlated with up-regulation of matrix metalloproteinase-13 in keloid regression after flashlamp pulsed-dye laser treatment.

BACKGROUND AND OBJECTIVES: Flashlamp pulsed-dye lasers (PDLs) has shown effectiveness in the treatment of keloids. In this study, we investigated whether PDL treatments decreased transforming growth factor-beta1 (TGF-beta1)-induction and up-regulation of matrix metalloproteinase (MMP) expression in keloid regression. STUDY DESIGN/MATERIALS AND METHODS: Keloid tissues obtained from 10 patients with intra-lesional or punch biopsies before and 7 days after PDL treatments [fluence per pulse was 10-18 J/cm2 (mean 14.0 J/cm2)]. Immunohistochemical (IHC) staining of TGF-beta1 and MMP-1 and MMP-13 expressions in keloid tissue was performed. Western blot analysis of MMP-1 and MMP-13 expressions in extracellular matrix was evaluated. RESULTS: IHC staining indicated that expression of TGF-beta1 was significantly reduced in keloid tissues after PDL irradiation. MMP-13 but not MMP-1 expression on IHC staining significantly increased in extracellular matrix of keloid tissues after PDL treatment. Western blot analysis also showed MMP-13 but not MMP-1 significant increased in keloid tissues after PDL treatment. CONCLUSIONS: Regression of keloids regressed after PDL treatments are associated with down-regulation of TGF-beta1 expression and up-regulation of MMP-13 activity.