Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion.

MOTIVATION: Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed. RESULTS: In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.

KNIndex: a comprehensive database of physicochemical properties for k-tuple nucleotides.

With the development of high-throughput sequencing technology, the genomic sequences increased exponentially over the last decade. In order to decode these new genomic data, machine learning methods were introduced for genome annotation and analysis. Due to the requirement of most machines learning methods, the biological sequences must be represented as fixed-length digital vectors. In this representation procedure, the physicochemical properties of k-tuple nucleotides are important information. However, the values of the physicochemical properties of k-tuple nucleotides are scattered in different resources. To facilitate the studies on genomic sequences, we developed the first comprehensive database, namely KNIndex (https://knindex.pufengdu.org), for depositing and visualizing physicochemical properties of k-tuple nucleotides. Currently, the KNIndex database contains 182 properties including one for mononucleotide (DNA), 169 for dinucleotide (147 for DNA and 22 for RNA) and 12 for trinucleotide (DNA). KNIndex database also provides a user-friendly web-based interface for the users to browse, query, visualize and download the physicochemical properties of k-tuple nucleotides. With the built-in conversion and visualization functions, users are allowed to display DNA/RNA sequences as curves of multiple physicochemical properties. We wish that the KNIndex will facilitate the related studies in computational biology.

Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum.

The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.

Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model.

OBJECTIVES: To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6 months after surgery. METHODS: Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction. RESULTS: The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74 years) and continued to increase within one month after LT (+ 9.18 years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT. CONCLUSIONS: The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor. KEY POINTS: • The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.

Altered white matter structural connectivity in primary Sjögren's syndrome: a link-based analysis.

PURPOSE: Cognitive impairment has been revealed in primary Sjögren's syndrome (pSS). However, the underlying white matter structural connectivity (SC) changes have not been studied. This study aimed to investigate the altered white matter brain network in patients with pSS using diffusion tensor imaging (DTI). METHODS: Forty-one pSS patients and sixty matched healthy controls (HCs) underwent neuropsychological tests and the subsequent MRI examinations. The clinical data were gathered from the medical record. The structural brain network was established using DTI, and a link-based comparison was performed between patients with pSS and HCs (false discovery rate correction, P < 0.05). Furthermore, the mean fractional anisotropy (FA) of the altered SCs was correlated with the neuropsychological tests and clinical data in patients with pSS (Bonferroni correction, P < 0.05). RESULTS: Compared with HCs, patients with pSS mainly exhibited decreased SC in the frontal and parietal lobes and some parts of the temporal and occipital lobes. In addition, increased SC was found between the right caudate nucleus and right median cingulate/paracingulate gyri. Specifically, the reduced SC between the left middle temporal gyrus and left middle occipital gyrus was negatively correlated with white matter high signal intensity (WMH). CONCLUSIONS: Patients with pSS showed diffusely decreased SC mainly in the frontoparietal network and exhibited a negative correlation between the reduced SC and WMH. SC represents a potential biomarker for preclinical brain impairment in patients with pSS.

Identifying protein-protein interface via a novel multi-scale local sequence and structural representation.

BACKGROUND: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. RESULTS: In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average Irmsd value of 3.28Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On CAPRI targets, our method achieves average Irmsd value of 3.45Å and overall Fnat value of 46%, which improves upon Irmsd of 4.18Å and Fnat of 40% for ZRANK, and Irmsd of 5.12Å and Fnat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. CONCLUSION: Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.

Altered hippocampal GABA and glutamate levels and uncoupling from functional connectivity in multiple sclerosis.

There is growing evidence for dysfunctional glutamatergic excitation and/or gamma-aminobutyric acid (GABA)ergic inhibition in patients with multiple sclerosis (MS). Cognitive impairment may occur during the early stages of MS and hippocampal abnormalities have been suggested as biomarkers. However, researchers have not clearly determined whether changes in hippocampal GABA and glutamate (Glu) levels are associated with cognitive impairment and aberrant neural activity in patients with MS. We used magnetic resonance spectroscopy to measure GABA+ and Glu levels in the left hippocampal region of 29 patients with relapsing-remitting MS and 29 healthy controls (HCs). Resting-state functional connectivity (FC) with the hippocampus was also examined. Compared to HCs, patients exhibited significantly lower GABA+ and Glu levels, which were associated with verbal and visuospatial memory deficits, respectively. Patients also showed decreased FC strengths between the hippocampus and several cortical regions, which are located within the default mode network. Moreover, hippocampal GABA+ levels and Glu/GABA+ ratios correlated with the FC strengths in HCs but not in patients with MS. This study describes a novel method for investigating the complex relationships among excitatory/inhibitory neurotransmitters, brain connectivity and cognition in health and disease. Strategies that modulate Glu and GABA neurotransmission may represent new therapeutic treatments for patients with MS.

Investigating the neural basis of basic human movement perception using multi-voxel pattern analysis.

Humans can skillfully recognize actions from others' body motion and make a judgment or response at once. Previous neuroimaging studies have mostly utilized diminished and brief human motion stimuli and indicated that human occipito-temporal cortex plays a critical role at biological motion recognition. It remains unclear to what extent that the areas related to human motion perception are involved in decoding basic movements. Because human movement naturally stems from the sequences of body posture, so we utilized the stimulus of real movements. Participants were presented four categories of human movements (jump, run, skip and walk) in a blocked fMRI experiment. Multi-voxel pattern analysis (MVPA) was adopted to assess whether different movements could be discriminated in four regions. We found that movement-specific information was represented in both human body-sensitive areas, extrastriate body area (EBA) and motion-sensitive areas, posterior superior temporal sulcus (pSTS) and human middle temporal complex (hMT+). Additionally, a further functional connectivity analysis using EBA as a seed was conducted and it suggested that EBA showed a task-modulated functional connectivity with multiple areas that were involved in the behavior perception and motor control. Human motion processing appeared to be completed in a distributed network. The occipito-temporal cortex may perform the initial processing of human motion information extracting, and then transform them to interconnected areas for a further utilization.

Heritability of the Effective Connectivity in the Resting-State Default Mode Network.

The default mode network (DMN) is thought to reflect endogenous neural activity, which is considered as one of the most intriguing phenomena in cognitive neuroscience. Previous studies have found that key regions within the DMN are highly interconnected. Here, we characterized the genetic influences on causal or directed information flow within the DMN during the resting state. In this study, we recruited 46 pairs of twins and collected fMRI imaging data using a 3.0 T scanner. Dynamic causal modeling was conducted for each participant, and a structural equation model was used to calculate the heritability of DMN in terms of its effective connectivity. Model comparison favored a full-connected model. Structural equal modeling was used to estimate the additive genetics (A), common environment (C) and unique environment (E) contributions to variance for the DMN effective connectivity. The ACE model was preferred in the comparison of structural equation models. Heritability of DMN effective connectivity was 0.54, suggesting that the genetic made a greater contribution to the effective connectivity within DMN. Establishing the heritability of default-mode effective connectivity endorses the use of resting-state networks as endophenotypes or intermediate phenotypes in the search for the genetic basis of psychiatric or neurological illnesses.

Decoding facial expressions based on face-selective and motion-sensitive areas.

Humans can easily recognize others' facial expressions. Among the brain substrates that enable this ability, considerable attention has been paid to face-selective areas; in contrast, whether motion-sensitive areas, which clearly exhibit sensitivity to facial movements, are involved in facial expression recognition remained unclear. The present functional magnetic resonance imaging (fMRI) study used multi-voxel pattern analysis (MVPA) to explore facial expression decoding in both face-selective and motion-sensitive areas. In a block design experiment, participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) in images, videos, and eyes-obscured videos. Due to the use of multiple stimulus types, the impacts of facial motion and eye-related information on facial expression decoding were also examined. It was found that motion-sensitive areas showed significant responses to emotional expressions and that dynamic expressions could be successfully decoded in both face-selective and motion-sensitive areas. Compared with static stimuli, dynamic expressions elicited consistently higher neural responses and decoding performance in all regions. A significant decrease in both activation and decoding accuracy due to the absence of eye-related information was also observed. Overall, the findings showed that emotional expressions are represented in motion-sensitive areas in addition to conventional face-selective areas, suggesting that motion-sensitive regions may also effectively contribute to facial expression recognition. The results also suggested that facial motion and eye-related information played important roles by carrying considerable expression information that could facilitate facial expression recognition. Hum Brain Mapp 38:3113-3125, 2017. © 2017 Wiley Periodicals, Inc.

Cross-modal integration of polyphonic characters in Chinese audio-visual sentences: a MVPA study based on functional connectivity.

This study aimed to investigate the functional connectivity in the brain during the cross-modal integration of polyphonic characters in Chinese audio-visual sentences. The visual sentences were all semantically reasonable and the audible pronunciations of the polyphonic characters in corresponding sentences contexts varied in four conditions. To measure the functional connectivity, correlation, coherence and phase synchronization index (PSI) were used, and then multivariate pattern analysis was performed to detect the consensus functional connectivity patterns. These analyses were confined in the time windows of three event-related potential components of P200, N400 and late positive shift (LPS) to investigate the dynamic changes of the connectivity patterns at different cognitive stages. We found that when differentiating the polyphonic characters with abnormal pronunciations from that with the appreciate ones in audio-visual sentences, significant classification results were obtained based on the coherence in the time window of the P200 component, the correlation in the time window of the N400 component and the coherence and PSI in the time window the LPS component. Moreover, the spatial distributions in these time windows were also different, with the recruitment of frontal sites in the time window of the P200 component, the frontal-central-parietal regions in the time window of the N400 component and the central-parietal sites in the time window of the LPS component. These findings demonstrate that the functional interaction mechanisms are different at different stages of audio-visual integration of polyphonic characters.

Development of the human fetal hippocampal formation during early second trimester.

Development of the fetal hippocampal formation has been difficult to fully describe because of rapid changes in its shape during the fetal period. The aims of this study were to: (1) segment the fetal hippocampal formation using 7.0 T MR images from 41 specimens with gestational ages ranging from 14 to 22 weeks and (2) reveal the developmental course of the fetal hippocampal formation using volume and shape analyses. Differences in hemispheric volume were observed, with the right hippocampi being larger than the left. Absolute volume changes showed a linear increase, while relative volume changes demonstrated an inverted-U shape trend during this period. Together these exhibited a variable developmental rate among different regions of the fetal brain. Different sub-regional growth of the fetal hippocampal formation was specifically observed using shape analysis. The fetal hippocampal formation possessed a prominent medial-lateral bidirectional shape growth pattern during its rotation process. Our results provide additional insight into 3D hippocampal morphology in the assessment of fetal brain development and can be used as a reference for future hippocampal studies.

Increasing breadth of the frontal lobe but decreasing height of the human brain between two Chinese samples from a Neolithic site and from living humans.

Morphological observation and measurements of endocasts have played a vital role in research on the evolution of the human brain. However, endocasts have never been used to investigate how the human brain has evolved since the Neolithic period. We investigated the evolution of the human brain during the Holocene by comparing virtual endocasts from Beiqian site (a Neolithic Chinese site) and a sample of Chinese modern-day humans. Standardized measurements and indices were taken to provide quantification of the overall endocast shape, including the length, breadth, height, frontal breadth, and the ratio of frontal breadth to breadth, as well as the cranial capacity. We found that the height of the endocasts and cranial capacity have decreased between our two samples, whereas the frontal breadth and sexual dimorphism have increased. We argue that these changes can be caused by random genetic mutation and epigenetic change in response to changes in the environment.

Inferior frontal white matter asymmetry correlates with executive control of attention.

White matter (WM) asymmetries of the human brain have been well documented using diffusion tensor imaging (DTI). However, the relationship between WM asymmetry pattern and cognitive performance is poorly understood. By means of tract-based spatial statistics (TBSS) and voxel-based analyses of whole brain, this study examined the WM asymmetries and the correlations between WM integrity/asymmetries and three distinct components of attention, namely alerting, orienting, and executive control (EC), which were assessed by attention network test (ANT). We revealed a number of WM anisotropy asymmetries, including leftward asymmetry of cingulum, corticospinal tract and cerebral peduncle, rightward asymmetry of internal capsule, superior longitudinal fasciculus and posterior corona radiata, as well as heterogeneous asymmetries in anterior corpus callosum and anterior corona radiata (ACR). Moreover, specific correlation was found between asymmetric pattern of inferior frontal ACR and EC performance. Additionally, this study also proposed that there were no significant relationships of WM anisotropy asymmetries to alerting and orienting functions. Further clusters of interest analyses and probabilistic fiber tracking validated our findings. In conclusion, there are a number of differences in WM integrity between human brain hemispheres. Specially, the anisotropy asymmetry in inferior frontal ACR plays a crucial role in EC function. Our finding is supportive of the functional studies of inferior frontal regions and in keeping with the theory of the brain lateralization on human ventral attention system.

[Effects of functional connectivity between anterior cingulate cortex and dorsolateral prefrontal cortex on executive control of attention in healthy individuals].

OBJECTIVE: To explore the presence of functional connectivity between anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC) during the manipulation of attentional network test (ANT) and its relationship with behavioral performance. METHODS: Functional magnetic resonance imaging was performed on 25 healthy subjects aged 17 - 20 years. And ANT was used as a paradigm. Functional connectivity between ACC-DLPFC was tested and correlation analysis conducted between functional connectivity coefficients and behavioral scores of ANT. RESULTS: Significant functional connectivity between the dorsal ACC (dACC) with bilateral DLPFC was found. Furthermore, event-related functional connectivity coefficients between left dACC and left DLPFC were negatively associated with the behavioral scores of executive control (r = -0.63; P < 0.01). CONCLUSION: Our findings provide new evidence that ACC and DLPFC are functionally connected and such functional connectivity has advantageous influence on executive control function of attention so as to contribute to our understanding of the integrated role of these brain regions in attentional network.

Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions.

Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.

Functional brain abnormalities in major depressive disorder using a multiscale community detection approach.

Major depressive disorder (MDD) is a serious disease associated with abnormal brain regions, however, the interconnection between specific brain regions related to depression has not been fully explored. To solve this problem, the paper proposes a novel multiscale community detection method to compare the differences in brain regions between normal controls (NC) and MDD patients. This study adopted the Brainnetome Atlas to divide the brain into 246 regions and extract the time series of each region. The Pearson correlation was used to measure the similarity among different brain regions to conduct the brain functional network and to perform multiscale community detection. The optimal brain community structure of each group was further explored based on the modularized Qcut algorithm, normalized mutual information (NMI), and variation of information (VI). The Jaccard index was then applied to compare the abnormalities of each brain region from different community environments between the brain function networks of NC and MDD patients. The experiments revealed several abnormal brain regions between NC and MDD, including the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, posterior superior temporal sulcus, inferior parietal gyrus, precuneus, postcentral gyrus, insular gyrus, cingulate gyrus, hippocampus and basal ganglia. Finally, a new subnetwork related to cognitive function was discovered, which was composed of the island gyrus and inferior frontal gyrus. All experiments indicated that the proposed method is useful in detecting functional brain abnormalities in MDD, and it can provide valuable insights into the diagnosis and treatment of MDD.

Integrative interaction of emotional speech in audio-visual modality.

Emotional clues are always expressed in many ways in our daily life, and the emotional information we receive is often represented by multiple modalities. Successful social interactions require a combination of multisensory cues to accurately determine the emotion of others. The integration mechanism of multimodal emotional information has been widely investigated. Different brain activity measurement methods were used to determine the location of brain regions involved in the audio-visual integration of emotional information, mainly in the bilateral superior temporal regions. However, the methods adopted in these studies are relatively simple, and the materials of the study rarely contain speech information. The integration mechanism of emotional speech in the human brain still needs further examinations. In this paper, a functional magnetic resonance imaging (fMRI) study was conducted using event-related design to explore the audio-visual integration mechanism of emotional speech in the human brain by using dynamic facial expressions and emotional speech to express emotions of different valences. Representational similarity analysis (RSA) based on regions of interest (ROIs), whole brain searchlight analysis, modality conjunction analysis and supra-additive analysis were used to analyze and verify the role of relevant brain regions. Meanwhile, a weighted RSA method was used to evaluate the contributions of each candidate model in the best fitted model of ROIs. The results showed that only the left insula was detected by all methods, suggesting that the left insula played an important role in the audio-visual integration of emotional speech. Whole brain searchlight analysis, modality conjunction analysis and supra-additive analysis together revealed that the bilateral middle temporal gyrus (MTG), right inferior parietal lobule and bilateral precuneus might be involved in the audio-visual integration of emotional speech from other aspects.

Structure-activity relationship studies on O-alkylamino-tethered salicylamide derivatives with various amino acid linkers as potent anticancer agents.

In our continued SAR study efforts, a series of O-alkylamino-tethered salicylamide derivatives with various amino acid linkers has been designed, synthesized, and biologically evaluated as potent anticancer agents. Five selected compounds with different representative chemical structures were found to show broad anti-proliferative activities, effective against all tested ER-positive breast cancer (BC) and triple-negative breast cancer (TNBC) cell lines with low micromolar IC50 values. Among these compounds, compound 9a (JMX0293) maintained good potency against MDA-MB-231 cell line (IC50 = 3.38 ± 0.37 µM) while exhibiting very low toxicity against human non-tumorigenic breast epithelial cell line MCF-10A (IC50 > 60 µM). Further mechanistic studies showed that compound 9a could inhibit STAT3 phosphorylation and contribute to apoptosis in TNBC MDA-MB-231 cells. More importantly, compound 9a significantly suppressed MDA-MB-231 xenograft tumor growth in vivo without significant toxicity, indicating its great potential as a promising anticancer drug candidate for further clinical development.

Passive static stretching alters the characteristics of the force-velocity curvature differently for fast and slow muscle groups-A practical application of Hill's equation.

Studies showed fast muscle fibers have a greater constant b value of Hill's equation than that of slow muscle fibers, and the changing ratio of b/Vmax indicates the altered characteristics of muscles under certain conditions such as static stretching. This study was to investigate the effect of acute passive static stretching on the curvature of force-velocity curve in people with different muscle fiber types. A two-step work was conducted in current study through using Hill's equation: 1) calculated b values for each subject at different conditions (non-stretched and stretched) to determine muscle groups, and 2) examined the effect of static stretching on different muscle groups. Sixty-five college students performed isokinetic leg extensions at 5 speeds to test peak torque, following either a non-stretching or two passive static quadriceps stretching exercises. The peak torque and corresponding velocity were used to calculate the b constant. Data reduction consisted of calculating a Z score for each non-stretched and stretched b values. Individuals, whose non-stretched b constant was above or below one standard deviation of the Z score, were designated as the less curved (fast) and more curved (slow) groups, respectively. A paired t-test was used to analyze the pre and post intervention effect on b values for each group (p < 0.05). This study found passive static stretching significantly altered the b constant of the fast group, but no effect on slow group. Therefore, we suggest static stretching should be avoided immediately before fast or explosive activities in individuals using predominantly fast muscle fibers.