The relation between size and parasite load in the Molly fish (Poecilia latipinna) of Jarghoyeh qanat, Isfahan Iran

A total of 30 Molly fish with an average weight and length of 1.5, and 5.5 cm were collected from Jarghoyeh qanat of Isfahan province 2013. Different organs of the fish including eyes, skin, gills and intestines were examined. The results showed that 36.6% and 100% were infected by Costia and while all were infected by Ich parasite. The results did not show any correlation between fish weight and Costia load However there was positive correlation between fish weight and Ich parasite loadin thegills

Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors

Motor function loss greatly impacts post-stroke survivors while performing activities of daily living. In the recent years, intelligent rehabilitation robotics have been proposed to enable the patients recover their lost limb functions. Besides, a large proportion of these robots function in passive mode that only allow users to navigate trajectories that rarely align with their limb movement intent, thus precluding full functional recovery. A potential solution would be to explore utilizing an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) to decode multiple classes of post-stroke patients’ motion intentions towards realizing dexterously active robotic training during rehabilitation. In this regard, we propose and examined for the first time, the use of Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns of stroke patients to provide adequate input for active motor training in rehabilitation robots. Importantly, we examined the proposed (STD-CWT) method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. Our method was validated using electromyogram signals of five stroke survivors who performed up to twenty-two distinct limb motions. The obtained results showed that the proposed technique recorded a significantly higher decoding (p<0.05) and converges faster compared to the commonly adopted method. The proposed method equally recorded obvious class separability for individual movement classes across the stroke patients. Findings from this study suggest that the STD-CWT Scalograms would provide potential inputs for robust decoding of motor intent that may facilitate intuitively active motor training in stroke rehabilitation robots. © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens.

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes

Assessment of the Conservation Status and Habitat Suitability of Critically Endangered Lorestan Newt (Neurergus Kaiseri) in Lorestan and Khuzestan Provinces

Lorestan newt (Neurergus kaiseri) is an endemic species which has restricted dispersal in southern Zagros Mountains in Iran, and it is listed as critically endangered in IUCN red list. Today the populations of this species are decreasing and facing serious threats. In this study, habitat suitability of Neurergus kaiseri was evaluated using a MaxEnt modelling approach according to environmental and climatic parameters (thermal and precipitation). Based on the results derived from the MaxEnt model, the most important parameters were related to annual and seasonality precipitation, annual mean temperature, elevation and land cover, respectively. Also, assessment of the conservation status of this species with species distribution areas and adapting them with protection networks revealed that currently, none of the suitable habitats of Lorestan newt are protected and there is no legal support for conserving these sites that the issue makes this critically endangered species even more vulnerable

An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running

The smooth interaction and coordination between lower-limb amputees and their prosthetics is crucial when performing complex tasks such as running. To address this, simultaneous and proportional control (SPC) based on continuous estimation of joint angles through surface electromyogram (sEMG) signals offers a more seamless interaction between users and their prosthetic limbs than conventional pattern recognition-based control, which relies on recognizing pre-defined movement classes from specific sEMG patterns using classification algorithms. This study proposes a deep learning-based model (AM-BiLSTM) that integrates the attention mechanism (AM) and bidirectional long short-term memory (BiLSTM) network to provide an accurate and robust SPC for estimating knee joint angle during running from a minimal number of sEMG sensors. The sEMG signals of four muscles recorded from 14 subjects during treadmill running at various speeds were utilized by the AM-BiLSTM model to decode knee joint kinematics. To comprehensively investigate the generalizability of the proposed model, it was tested in intra-subject and speed, intra-subject and inter-speed, and inter-subject and speed scenarios and compared with BiLSTM, standard LSTM, and multi-layer perceptron (MLP) approaches. Normalized root-mean-square error and correlation coefficient were used as performance metrics. According to nonparametric statistical tests, the proposed AM-BiLSTM model significantly outperformed the BiLSTM network as well as classical techniques including LSTM and MLP networks in all three experiments (p-value < 0.05) and achieved state-of-the-art performance. Based on our findings, the AM-BiLSTM model may facilitate the deployment of intuitive user-driven deep learning-based control schemes for a wide variety of miniaturized robotic lower-limb devices, including myoelectric prostheses

Poloxamer: A versatile tri-block copolymer for biomedical applications

Poloxamers, also called Pluronic, belong to a unique class of synthetic tri-block copolymers containing central hydrophobic chains of poly(propylene oxide) sandwiched between two hydrophilic chains of poly(ethylene oxide). Some chemical characteristics of poloxamers such as temperature-dependent self-assembly and thermo-reversible behavior along with biocompatibility and physiochemical properties make poloxamer-based biomaterials promising candidates for biomedical application such as tissue engineering and drug delivery. The microstructure, bioactivity, and mechanical properties of poloxamers can be tailored to mimic the behavior of various types of tissues. Moreover, their amphiphilic nature and the potential to self-assemble into the micelles make them promising drug carriers with the ability to improve the drug availability to make cancer cells more vulnerable to drugs. Poloxamers are also used for the modification of hydrophobic tissue-engineered constructs. This article collects the recent advances in design and application of poloxamer-based biomaterials in tissue engineering, drug/gene delivery, theranostic devices, and bioinks for 3D printing. Statement of significance: Poloxamers, also called Pluronic, belong to a unique class of synthetic tri-block copolymers containing central hydrophobic chains of poly(propylene oxide) sandwiched between two hydrophilic chains of poly(ethylene oxide). The microstructure, bioactivity, and mechanical properties of poloxamers can be tailored to mimic the behavior of various types of tissues. Moreover, their amphiphilic nature and the potential to self-assemble into the micelles make them promising drug carriers with the ability to improve the drug availability to make cancer cells more vulnerable to drugs. However, no reports have systematically reviewed the critical role of poloxamer for biomedical applications. Research on poloxamers is growing today opening new scenarios that expand the potential of these biomaterials from �traditional� treatments to a new era of tissue engineering. To the best of our knowledge, this is the first review article in which such issue is systematically reviewed and critically discussed in the light of the existing literature. © 2020 Acta Materialia Inc

Conductive biomaterials as nerve conduits: Recent advances and future challenges

Millions of people around the world are in distress due to neurodegenerative disorders. There have been continued attempts to design biomaterial-based therapies for the regeneration of dysfunctional neural tissues, mainly damaged peripheral nerve and spinal cord. The development of nerve guidance channels, where the distal and proximal end of a damaged nerve is sutured to an artificial conduit, has been one main strategy to treat damaged nerves. Different types of biomaterials have been utilized for fabricating the functional nerve conduits with the capability to stimulate the cellular function. Due to their intrinsic electrical properties, conductive materials revealed promising features for promoting regeneration of peripheral nerve injuries. This review article aims to critically summarize the recent advances and challenges toward the development of nerve conduits based on conductive materials and their future clinical applications. © 2020 Elsevier Lt