Bio-inspired models based on the lobula giant movement detector (LGMD) in the locust\'s visual brain have received extensive attention and application for collision perception in various scenarios. These models offer advantages such as low power consumption and high computational efficiency in visual processing. However, current LGMD-based computational models, typically organized as four-layered neural networks, often encounter challenges related to noisy signals, particularly in complex dynamic environments. Biological studies have unveiled the intrinsic stochastic nature of synaptic transmission, which can aid neural computation in mitigating noise. In alignment with these biological findings, this paper introduces a probabilistic LGMD (Prob-LGMD) model that incorporates a probability into the synaptic connections between multiple layers, thereby capturing the uncertainty in signal transmission, interaction, and integration among neurons. Comparative testing of the proposed Prob-LGMD model and two conventional LGMD models was conducted using a range of visual stimuli, including indoor structured scenes and complex outdoor scenes, all subject to artificial noise. Additionally, the model\'s performance was compared to standard engineering noise-filtering methods. The results clearly demonstrate that the proposed model outperforms all comparative methods, exhibiting a significant improvement in noise tolerance. This study showcases a straightforward yet effective approach to enhance collision perception in noisy environments.

Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly evaluated, identifying their common meta-properties that are essential for their functionality. This article reveals a common framework, characterized by layered structures and computational strategies, which is crucial for enhancing the capability of bio-inspired models for diverse applications. The result of this analysis is the Strategic Prototype, which embodies the identified meta-properties. It represents a modular and more flexible method for developing more responsive and adaptable robotic visual systems. The perspective highlights the potential of the Strategic Prototype: LGMD-Universally Prototype (LGMD-UP), the key to re-framing LGMD models and advancing our understanding and implementation of bio-inspired visual systems in robotics. It might open up more flexible and adaptable avenues for research and practical applications.

Visual perception equips unmanned aerial vehicles (UAVs) with increasingly comprehensive and instant environmental perception, rendering it a crucial technology in intelligent UAV obstacle avoidance. However, the rapid movements of UAVs cause significant changes in the field of view, affecting the algorithms\' ability to extract the visual features of collisions accurately. As a result, algorithms suffer from a high rate of false alarms and a delay in warning time. During the study of visual field angle curves of different orders, it was found that the peak times of the curves of higher-order information on the angular size of looming objects are linearly related to the time to collision (TTC) and occur before collisions. This discovery implies that encoding higher-order information on the angular size could resolve the issue of response lag. Furthermore, the fact that the image of a looming object adjusts to meet several looming visual cues compared to the background interference implies that integrating various field-of-view characteristics will likely enhance the model\'s resistance to motion interference. Therefore, this paper presents a concise A-LGMD model for detecting looming objects. The model is based on image angular acceleration and addresses problems related to imprecise feature extraction and insufficient time series modeling to enhance the model\'s ability to rapidly and precisely detect looming objects during the rapid self-motion of UAVs. The model draws inspiration from the lobula giant movement detector (LGMD), which shows high sensitivity to acceleration information. In the proposed model, higher-order information on the angular size is abstracted by the network and fused with multiple visual field angle characteristics to promote the selective response to looming objects. Experiments carried out on synthetic and real-world datasets reveal that the model can efficiently detect the angular acceleration of an image, filter out insignificant background motion, and provide early warnings. These findings indicate that the model could have significant potential in embedded collision detection systems of micro or small UAVs.

We report the case of a 31-year-old Chinese woman with a chief complaint of weakness in the lower limbs, which was diagnosed as limb-girdle muscular dystrophy 2B (LGMD2B) with compound heterozygous mutations of the DYSF gene. Meanwhile, this woman is an asymptomatic carrier with the mutation of the X-linked DMD gene. The electromyography, muscle MRI, and muscle biopsy indicated a chronic myogenic injury with dysferlin deletion. As a result of genetic testing, compound heterozygous G-to-T base substitution at position 5,497 in exon 49 of the DYSF gene, leading to a codon change from glutamic acid to termination codon at position 1,833, and a heterozygous C-to-G base change at position 4,638 + 8 in intron 42 of the DYSF gene with a consequence of splice, which has never been reported, were identified as candidate causative mutations. Unfortunately, DMD gene mutation c.3921+12A>G of the DMD gene on the X chromosome was also found in this patient. Finally, the patient was diagnosed as LGMD2B clinically and genetically. In the previous 2 years, the patient\'s lower limb weakness became slightly worse, resulting in even the total distance walked than before. Fortunately, during the follow-up, her son had not shown slowness or limitation of movement. Genetic testing by next-generation sequencing confirmed the final diagnosis of LGMD2B, and we identified the novel compound heterozygous variants in the DYSF gene, which is of great significance to the accurate diagnosis of genetically coded diseases. Much attention needs to be paid in clinics toward hereditary neuromuscular diseases with multiple pathogenic gene mutations. Genetic counseling and clinical follow-up should be the priorities in future, and promising treatments are also worth exploring.

Physiological studies have shown that a group of locust\'s lobula giant movement detectors (LGMDs) has a diversity of collision selectivity to approaching objects, relatively darker or brighter than their backgrounds in cluttered environments. Such diversity of collision selectivity can serve locusts to escape from attack by natural enemies, and migrate in swarm free of collision. For computational studies, endeavours have been made to realize the diverse selectivity which, however, is still one of the most challenging tasks especially in complex and dynamic real world scenarios. The existing models are mainly formulated as multi-layered neural networks with merely feed-forward information processing, and do not take into account the effect of re-entrant signals in feedback loop, which is an essential regulatory loop for motion perception, yet never been explored in looming perception. In this paper, we inaugurate feedback neural computation for constructing a new LGMD-based model, named F-LGMD to look into the efficacy upon implementing different collision selectivity. Accordingly, the proposed neural network model features both feed-forward processing and feedback loop. The feedback control propagates output signals of parallel ON/OFF channels back into their starting neurons, thus makes part of the feed-forward neural network, i.e. the ON/OFF channels and the feedback loop form an iterative cycle system. Moreover, the feedback control is instantaneous, which leads to the existence of a fixed point whereby the fixed point theorem is applied to rigorously derive valid range of feedback coefficients. To verify the effectiveness of the proposed method, we conduct systematic experiments covering synthetic and natural collision datasets, and also online robotic tests. The experimental results show that the F-LGMD, with a unified network, can fulfil the diverse collision selectivity revealed in physiology, which not only reduces considerably the handcrafted parameters compared to previous studies, but also offers a both efficient and robust scheme for collision perception through feedback neural computation.

The Popeye domain-containing protein 3 (POPDC3), a transmembrane protein with a unique cyclic adenosine monophosphate (cAMP) binding site, is widely expressed in mammalian tissues, with the highest levels of expression in skeletal muscle. POPDC3 plays a key role in many physiological and pathological processes and is considered a candidate biomarker and potential therapeutic target of cancer. In addition, POPDC3 gene variants have been associated with limb-girdle muscular dystrophy (LGMD) type 26. However, there are only a few studies on the biological role of POPDC3, interacting proteins, potential downstream targets, and regulated signaling pathways. Therefore, this review focuses on the structure of POPDC3 protein, interacting molecules, and the role and mechanism in cancer, and in cardiac and skeletal muscle, and to review the current research progress of POPDC3 and propose possible future study directions.

Limb-girdle muscular dystrophy (LGMD) comprises a heterogeneous group of diseases, affecting different muscles, predominantly skeletal muscles and cardiac muscles of the body. LGMD is classified into two main subtypes A and B, which are further subclassified into eight dominant and thirty recessive subtypes. Three genes, namely POPDC1, POPDC2 and POPDC3, encode popeye domain-containing protein (POPDC), and the variants of POPDC1 and POPDC3 genes have been associated with LGMD.
In the present study, we performed whole-exome sequencing (WES) analysis on a single-family to investigate the hallmark features of LGMD. The results of WES were further confirmed by Sanger sequencing and 3D protein modeling was also conducted.
WES data analysis and Sanger sequencing revealed a homozygous missense variant (c.460A>G; p.Lys154Glu) at a highly conserved amino acid position in the POPDC3. Mutations in the POPDC3 gene have been previously associated with recessive limb-girdle muscular dystrophy type 26. 3D protein modeling further suggested that the identified variant might affect the POPDC3 structure and proper function.
The present study confirms the role of POPDC3 in LGMD, and will facilitate genetic counseling of the family to mitigate the risks of the carrier or affects on future pregnancies.

Limb-girdle muscular dystrophies (LGMDs) are large group of heterogeneous genetic diseases, having a hallmark feature of muscle weakness. Pathogenic mutations in the gene encoding the giant skeletal muscle protein titin (TTN) are associated with several muscle disorders, including cardiomyopathy, recessive congenital myopathies and limb-girdle muscular dystrophy (LGMD) type10. The phenotypic spectrum of titinopathies is expanding, as next generation sequencing (NGS) technology makes screening of this large gene possible.
This study aimed to identify the pathogenic variant in a consanguineous Pakistani family with autosomal recessive LGMD type 10.
DNA from peripheral blood samples were obtained, whole exome sequencing (WES) was performed and several molecular and bioinformatics analysis were conducted to identify the pathogenic variant. TTN coding and near coding regions were further amplified using PCR and sequenced via Sanger sequencing.
Whole exome sequencing analysis revealed a novel homozygous missense variant (c.98807G > A; p.Arg32936His) in the TTN gene in the index patients. No heterozygous individuals in the family presented LGMD features. The variant p.Arg32936His leads to a substitution of the arginine amino acid at position 32,936 into histidine possibly causing LGMD type 10.
We identified a homozygous missense variant in TTN, which likely explains LGMD type 10 in this family in line with similar previously reported data. Our study concludes that WES is a successful molecular diagnostic tool to identify pathogenic variants in large genes such as TTN in highly inbred population.

Background: Limb-girdle muscular dystrophy (LGMD) is an increasingly heterogeneous category of inherited muscle diseases, mainly affecting the muscles of shoulder areas and the hip, segregating in both autosomal recessive and dominant manner. To-date, thirty-one loci have been identified for LGMD including seven autosomal dominant (LGMD type 1) and twenty four autosomal recessive (LGMD type 2) inherited loci. Methodology/Laboratory Examination: The present report describes a consanguineous family segregating LGMD2F in an autosomal recessive pattern. The affected individual is an 11-year-old boy having two brothers and a sister. Direct targeted next generation sequencing was performed for the single affected individual (VI-1) followed by Sanger sequencing. Results: Targeted next generation sequencing revealed a novel homozygous nonsense mutation (c.289C>T; p.Arg97∗) in the exon 3 of the delta-sarcoglycan (SGCD) gene, that introduces a premature stop codon (TCA), resulting in a nonsense mediated decay or a truncated protein product. Discussion and Conclusion: This is the first report of LGMD2F caused by an SGCD variant in a Pakistani population. The mutation identified in the present investigation extends the body of evidence implicating the gene SGCD in causing LGMD2F and might help in genetic counseling, which is more important to deliver the risk of carrier or affected in the future pregnancies.