Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Local Clusters of Mutation Hotspots in Cancer-Related Genes and Their Biological Relevance

Mutation hotspots are either solitary amino acid residues or stretches of amino acids that show elevated mutation frequency in cancer-related genes, but their prevalence and biological relevance are not completely understood. Here, we developed a Smith-Waterman algorithm-based mutation hotspot discovery method, MutClustSW, to identify mutation hotspots of either single or clustered amino acid residues. We identified 181 missense mutation hotspots from COSMIC and TCGA mutation databases. In addition to 77 single amino acid residue hotspots (42.5 percent) including well-known mutation hotspots such as IDH1 (p.R132) and BRAF (p.V600), we identified 104 mutation hotspots (57.5 percent) as clusters or stretches of multiple amino acids, and the hotspots on MUC2, EPPK1, KMT2C, and TP53 were larger than 50 amino acids. Twelve of 27 nonsense mutation hotspots (44.4 percent) were observed in four cancer-related genes, TP53, ARID1A, CDKN2A, and PTEN, suggesting that truncating mutations on some tumor suppressor genes are not randomly distributed as previously assumed. We also show that hotspot mutations have higher mutation allele frequency than non-hotspots, and the hotspot information can be used to prioritize the cancer drivers. Together, the proposed algorithm and the mutation hotspot information can serve as valuable resources in the selection of functional driver mutations and associated genes.
A Scalar Projection and Angle-Based Evolutionary Algorithm for Many-Objective Optimization Problems

In decomposition-based multiobjective evolutionary algorithms, the setting of search directions (or weight vectors), and the choice of reference points (i.e., the ideal point or the nadir point) in scalarizing functions, are of great importance to the performance of the algorithms. This paper proposes a new decomposition-based many-objective optimizer by simultaneously using adaptive search directions and two reference points. For each parent, binary search directions are constructed by using its objective vector and the two reference points. Each individual is simultaneously evaluated on two fitness functions—which are motivated by scalar projections—that are deduced to be the differences between two penalty-based boundary intersection (PBI) functions, and two inverted PBI functions, respectively. Solutions with the best value on each fitness function are emphasized. Moreover, an angle-based elimination procedure is adopted to select diversified solutions for the next generation. The use of adaptive search directions aims at effectively handling problems with irregular Pareto-optimal fronts, and the philosophy of using the ideal and nadir points simultaneously is to take advantages of the complementary effects of the two points when handling problems with either concave or convex fronts. The performance of the proposed algorithm is compared with seven state-of-the-art multi-/many-objective evolutionary algorithms on 32 test problems with up to 15 objectives. It is shown by the experimental results that the proposed algorithm is flexible when handling problems with different types of Pareto-optimal fronts, obtaining promising results regarding both the quality of the returned solution set and the efficiency of the new algorithm.
Robust 3D Human Pose Estimation from Single Images or Video Sequences

We propose a method for estimating 3D human poses from single images or video sequences. The task is challenging because: (a) many 3D poses can have similar 2D pose projections which makes the lifting ambiguous, and (b) current 2D joint detectors are not accurate which can cause big errors in 3D estimates. We represent 3D poses by a sparse combination of bases which encode structural pose priors to reduce the lifting ambiguity. This prior is strengthened by adding limb length constraints. We estimate the 3D pose by minimizing an <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq1-2828427.gif"/></alternatives></inline-formula> norm measurement error between the 2D pose and the 3D pose because it is less sensitive to inaccurate 2D poses. We modify our algorithm to output <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-2828427.gif"/></alternatives></inline-formula> 3D pose candidates for an image, and for videos, we impose a temporal smoothness constraint to select the best sequence of 3D poses from the candidates. We demonstrate good results on 3D pose estimation from static images and improved performance by selecting the best 3D pose from the <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq3-2828427.gif"/></alternatives></inline-formula> proposals. Our results on video sequences also show improvements (over static images) of roughly 15%.
Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain–computer interface (BCI) application. The effectiveness of CSP is highly affected by the frequency band and time window of EEG segments. Although numerous algorithms have been designed to optimize the spectral bands of CSP, most of them selected the time window in a heuristic way. This is likely to result in a suboptimal feature extraction since the time period when the brain responses to the mental tasks occurs may not be accurately detected. In this paper, we propose a novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG. Specifically, spectrum-specific signals are first derived by bandpass filtering from raw EEG data at a set of overlapping filter bands. Each of the spectrum-specific signals is further segmented into multiple subseries using sliding window approach. We then devise a joint sparse optimization of filter bands and time windows with temporal smoothness constraint to extract robust CSP features under a multitask learning framework. A linear support vector machine classifier is trained on the optimized EEG features to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI Competition III dataset IIIa, BCI Competition IV datasets IIa, and BCI Competition IV dataset IIb) to validate the effectiveness of TSGSP in comparison to several other competing methods. Superior classification performance (averaged accuracies are 88.5%, 83.3%, and 84.3% for the three datasets, respectively) based on the experimental results confirms that the proposed algorithm is a promising candidate for performance improvement of MI-based BCIs.
Adaptive Estimated Inverse Output-Feedback Quantized Control for Piezoelectric Positioning Stage

Focusing on the piezoelectric positioning stage, this paper proposes an adaptive estimated inverse output-feedback quantized control scheme. First, the quantized issue due to the use of computer is addressed by introducing a linear time-varying quantizer model where the quantizer parameters can be estimated on-line. Second, by using the fuzzy approximator, the developed controller can avoid the identification of the parameters in the piezoelectric positioning stage. Third, by constructing the estimated inverse compensator of the hysteresis, the hysteresis nonlinearities in the piezoelectric actuator are mitigated; Fourth, the states observer is designed to avoid the measurements of the velocity and acceleration signals. The analysis of stability shows all the signals in the piezoelectric positioning stage are uniformly ultimately bounded and the prespecified tracking performance of the quantized control system is achieved by employing the error transformed function. Finally, a computer controlled experiments for the piezoelectric positioning stage is conducted to show the effectiveness of the proposed quantized controller.
PTPα is required for laminin-2-induced Fyn–Akt signaling to drive oligodendrocyte differentiation

ABSTRACT Extrinsic signals that regulate oligodendrocyte maturation and subsequent myelination are essential for central nervous system development and regeneration. Deficiency in the extracellular factor laminin-2 (Lm2, comprising the α2β1γ1 chains), as occurs in congenital muscular dystrophy, can lead to impaired oligodendroglial development and aberrant myelination, but many aspects of Lm2-regulated oligodendroglial signaling and differentiation remain undefined. We show that receptor-like protein tyrosine phosphatase α (PTPα, also known as PTPRA) is essential for myelin basic protein expression and cell spreading during Lm2-induced oligodendrocyte differentiation. PTPα complexes with the Lm2 receptors α6β1 integrin and dystroglycan to transduce Fyn activation upon Lm2 engagement. In this way, PTPα mediates a subset of Lm2-induced signals required for differentiation, includeing mTOR-dependent Akt activation but not Erk1/2 activation. We identify N-myc downstream regulated gene-1 (NDRG1) as a PTPα-regulated molecule during oligodendrocyte differentiation, and distinguish Lm2 receptor-specific modes of Fyn–Akt-dependent and -independent NDRG1 phosphorylation. Altogether, this reveals an Lm2-regulated PTPα–Fyn–Akt signaling axis that is critical for key aspects of the gene expression and morphological changes that mark oligodendrocyte maturation. Summary: The receptor PTPα is an essential component of integrin and dystroglycan receptor complexes that transduce extrinsic laminin-2 signals into specific intracellular events to promote OL differentiation.
Tensor Based Temporal and Multilayer Community Detection for Studying Brain Dynamics During Resting State fMRI

Objective: In recent years, resting state fMRI has been widely utilized to understand the functional organization of the brain for healthy and disease populations. Recent studies show that functional connectivity during resting state is a dynamic process. Studying this temporal dynamics provides a better understanding of the human brain compared to static network analysis. Methods: In this paper, a new tensor based temporal and multi-layer community detection algorithm is introduced to identify and track the brain network community structure across time and subjects. The framework studies the temporal evolution of communities in fMRI connectivity networks constructed across different regions of interests. The proposed approach relies on determining the subspace that best describes the community structure using Tucker decomposition of the tensor. Results: The brain dynamics are summarized into a set of functional connectivity states that are repeated over time and subjects. The dynamic behavior of the brain is evaluated in terms of consistency of different subnetworks during resting state. The results illustrate that some of the networks, such as the default mode, cognitive control and bilateral limbic networks, have low consistency over time indicating their dynamic behavior. Conclusion: The results indicate that the functional connectivity of the brain is dynamic and the detected community structure experiences smooth temporal evolution. Significance: The work in this paper provides evidence for temporal brain dynamics during resting state through dynamic multi-layer community detection which enables us to better understand the behavior of different subnetworks.
A Versatile Lentiviral Delivery Toolkit for Proximity-dependent Biotinylation in Diverse Cell Types*

The proximity-dependent biotinylation approach BioID is increasingly used to define proximity interactomes and subcellular organization, but is most often limited to a few easily transfectable cell lines. Here, a versatile set of lentiviral delivery vectors that enable BioID across cell types is described that incorporates features such as inducible expression and selectable markers. Benchmarking of these vectors demonstrates their ease and versatility across primary and immortalized cell systems. The lentiviral vectors are available through the Network Biology Collaborative Centre (nbcc.lunenfeld.ca). Graphical Abstract Highlights Description of a set of lentiviral constructs for doxycycline-inducible BioID. All vector configurations enable amino- or carboxyl-terminal fusion in a Gateway system. Vector variants incorporate selectable markers (puromycin, fluorescent protein). Guidelines for using the vectors and designing BioID experiments are provided. Proximity-dependent biotinylation strategies have emerged as powerful tools to characterize the subcellular context of proteins in living cells. The popular BioID approach employs an abortive E. coli biotin ligase mutant (R118G; denoted as BirA*), which when fused to a bait protein enables the covalent biotinylation of endogenous proximal polypeptides. This approach has been mainly applied to the study of protein proximity in immortalized mammalian cell lines. To expand the application space of BioID, here we describe a set of lentiviral vectors that enable the inducible expression of BirA*-tagged bait fusion proteins for performing proximity-dependent biotinylation in diverse experimental systems. We benchmark this highly adaptable toolkit across immortalized and primary cell systems, demonstrating the ease, versatility and robustness of the system. We also provide guidelines to perform BioID using these reagents.
α-Synuclein oligomers induce early axonal dysfunction in human iPSC-based models of synucleinopathies

Significance α-Synuclein (α-Syn) aggregation underlies neurodegeneration in synucleinopathies. However, the nature of α-Syn aggregates and their toxic mechanisms in human pathology remains elusive. Here, we delineate a role of α-Syn oligomeric aggregates for axonal integrity in human neuronal models of synucleinopathies. α-Syn oligomers disrupt anterograde axonal transport of mitochondria by causing subcellular changes in transport-regulating proteins and energy deficits. An increase of α-Syn oligomers in human neurons finally results in synaptic degeneration. Together, our data provide mechanistic insights of α-Syn oligomeric toxicity in human neurons. Taking into account that α-Syn oligomers and axonal dysfunction are characteristic for early neurodegeneration in synucleinopathies, our data might deliver targets for therapeutic interference with early disease pathology. α-Synuclein (α-Syn) aggregation, proceeding from oligomers to fibrils, is one central hallmark of neurodegeneration in synucleinopathies. α-Syn oligomers are toxic by triggering neurodegenerative processes in in vitro and in vivo models. However, the precise contribution of α-Syn oligomers to neurite pathology in human neurons and the underlying mechanisms remain unclear. Here, we demonstrate the formation of oligomeric α-Syn intermediates and reduced axonal mitochondrial transport in human neurons derived from induced pluripotent stem cells (iPSC) from a Parkinson’s disease patient carrying an α-Syn gene duplication. We further show that increased levels of α-Syn oligomers disrupt axonal integrity in human neurons. We apply an α-Syn oligomerization model by expressing α-Syn oligomer-forming mutants (E46K and E57K) and wild-type α-Syn in human iPSC-derived neurons. Pronounced α-Syn oligomerization led to impaired anterograde axonal transport of mitochondria, which can be restored by the inhibition of α-Syn oligomer formation. Furthermore, α-Syn oligomers were associated with a subcellular relocation of transport-regulating proteins Miro1, KLC1, and Tau as well as reduced ATP levels, underlying axonal transport deficits. Consequently, reduced axonal density and structural synaptic degeneration were observed in human neurons in the presence of high levels of α-Syn oligomers. Together, increased dosage of α-Syn resulting in α-Syn oligomerization causes axonal transport disruption and energy deficits, leading to synapse loss in human neurons. This study identifies α-Syn oligomers as the critical species triggering early axonal dysfunction in synucleinopathies.
A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators

This paper presents a real-time electrocardiogram (ECG) monitoring system for wearable devices. The system is based on the proposed parallel delta modulator architecture with local maximum point and local minimum point algorithms to detect QRS and PT waves. Therefore, using the proposed system and algorithm, real-time PR and RT intervals, and ST segment measurements can be achieved in long-term wearable ECG recording. The algorithm is tested with the MIT-BIH Arrhythmia Database for QRS complex detection and with the QT Database for the P and T wave detections. The simulation result shows that the algorithm achieves above 99%, 91%, and 98% accuracy in the QRS complex, P wave, and T wave detections, respectively. Experimental results are presented from the system prototype, in which the parallel delta modulator circuits are fabricated in IBM 0.13 <inline-formula><tex-math notation="LaTeX">$\mu \text{m}$</tex-math></inline-formula> standard CMOS technology and the algorithms are implemented in a Xilinx Spartan-6 field programmable gate array (FPGA). The parallel delta modulators consume 720 nW at 1 kHz sampling rate with <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>0.6 V power supply. The proposed system has the potential to be applied in future long-term wearable ECG recording devices.
Passengers queue analysis in international airports terminals in Kerala using multiphase queuing system

Waiting period is a global problem that almost everyone has to face, which causes a great waste of time for everyone. It is well known that all these waiting line problems critically restrict further development. The focus of this study is to deal with passengers' queue issues of the international airport terminals of Kerala. Queuing theory is a mathematical approach to the study of waiting period in queues. This study evaluates the effectiveness of multi-server queuing model. The multi server approach of modelling was adopted in this cram to develop a mathematical model to solve problem of queuing of air transport passengers at the international airports in Kerala. The airport in the aviation industry of the country faces problems of many passengers queuing for boarding, departure with different arrival rate due to non availability of state of the art logistics management mechanisms for predicting the nature and service demands of travellers. The passengers' average wait time for reaching the gate area measures system performance. A mathematical queuing model was developed in this study and comparisons are made using analysis of variance (ANOVA).
Use of Actigraphy for the Evaluation of Sleep Disorders and Circadian Rhythm Sleep-Wake Disorders: An American Academy of Sleep Medicine Clinical Practice Guideline.

INTRODUCTION The purpose of this guideline is to establish clinical practice recommendations for the use of actigraphy in adult and pediatric patients with suspected or diagnosed sleep disorders or circadian rhythm sleep-wake disorders. METHODS The American Academy of Sleep Medicine (AASM) commissioned a task force of experts in sleep medicine to develop recommendations and assigned strengths based on a systematic review of the literature and an assessment of the evidence using the GRADE process. The task force provided a summary of the relevant literature and the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use considerations that support the recommendations. The AASM Board of Directors approved the final recommendations. RECOMMENDATIONS The following recommendations are intended as a guide for clinicians using actigraphy in evaluating patients with sleep disorders and circadian rhythm sleep-wake disorders, and only apply to the use of FDA-approved devices. Each recommendation statement is assigned a strength ("Strong" or "Conditional"). A "Strong" recommendation (ie, "We recommend…") is one that clinicians should follow under most circumstances. A "Conditional" recommendation (ie, "We suggest…") reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding any specific care must be made by the treating clinician and the patient, taking into consideration the individual circumstances of the patient, available treatment options, and resources. We suggest that clinicians use actigraphy to estimate sleep parameters in adult patients with insomnia disorder. (Conditional). We suggest that clinicians use actigraphy in the assessment of pediatric patients with insomnia disorder. (Conditional). We suggest that clinicians use actigraphy in the assessment of adult patients with circadian rhythm sleep-wake disorder. (Conditional). We suggest that clinicians use actigraphy in the assessment of pediatric patients with circadian rhythm sleep-wake disorder. (Conditional). We suggest that clinicians use actigraphy integrated with home sleep apnea test devices to estimate total sleep time during recording (in the absence of alternative objective measurements of total sleep time) in adult patients suspected of sleep-disordered breathing. (Conditional). We suggest that clinicians use actigraphy to monitor total sleep time prior to testing with the Multiple Sleep Latency Test in adult and pediatric patients with suspected central disorders of hypersomnolence. (Conditional). We suggest that clinicians use actigraphy to estimate total sleep time in adult patients with suspected insufficient sleep syndrome. (Conditional). We recommend that clinicians not use actigraphy in place of electromyography for the diagnosis of periodic limb movement disorder in adult and pediatric patients. (Strong).