Search results for: deep brain stimulation

Authors: Shoukat Ali, Amjad Hussain, Latif-ur-Rehman, Sehrish Inam

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Radiotherapy plays an important role in the management of cancer patients. Approximately 50% of the cancer patients receive radiotherapy at one point or another during the course of treatment. The entire radiotherapy treatment of curative intent is divided into different phases, depending on the histology of the tumor. The established protocols are useful in deciding the total dose, fraction size, and numbers of phases. The objective of this study was to evaluate the dosimetric differences between the conventional treatment protocols and the three-dimensional conformal simultaneously integrated boost (SIB) plans for three different tumors sites (i.e. bladder, breast, and brain). A total of 30 patients with brain, breast and bladder cancers were selected in this retrospective study. All the patients were CT simulated initially. The primary physician contoured PTV1 and PTV2 in the axial slices. The conventional doses prescribed for brain and breast is 60Gy/30 fractions, and 64.8Gy/36 fractions for bladder treatment. For the SIB plans biological effective doses (BED) were calculated for 25 fractions. The two conventional (Phase I and Phase II) and a single SIB plan for each patient were generated on Eclipse™ treatment planning system. Treatment plans were compared and analyzed for coverage index, conformity index, homogeneity index, dose gradient and organs at risk doses.In both plans 95% of PTV volume received a minimum of 95% of the prescribe dose. Dose deviation in the optic chiasm was found to be less than 0.5%. There is no significant difference in lung V20 and heart V30 in the breast plans. In the rectum plans V75%, V50% and V25% were found to be less than 1.2% different. Deviation in the tumor coverage, conformity and homogeneity indices were found to be less than 1%. SIB plans with three dimensional conformal radiotherapy technique reduce the overall treatment time without compromising the target coverage and without increasing dose to the organs at risk. The higher dose per fraction may increase the late effects to some extent. Further studies are required to evaluate the late effects with the intention of standardizing the SIB technique for practical implementation.

Keywords: coverage index, conformity index, dose gradient, homogeneity index, simultaneously integrated boost

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Ludmila Vucolova

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The thought is a process that underlies consciousness and cognition and understanding its origin and processes is a longstanding goal of many academic disciplines. By integrating over twenty novel ideas and hypotheses of this theoretical proposal, we can speculate that thought is an emergent property of coded neural events, translating the electro-chemical interactions of the body with its environment—the objects of sensory stimulation, X, and Y. The latter is a self- generated feedback entity, resulting from the arbitrary pattern of the motion of a body’s motor repertory (M). A culmination of these neural events gives rise to a thought: a state of identity between an observed object X and a symbol Y. It manifests as a “state of awareness” or “state of knowing” and forms our perception of the physical world. The values of the variables of a construct—X (object), S1 (sense for the perception of X), Y (object), S2 (sense for perception of Y), and M (motor repertory that produces Y)—will specify the particular conscious percept at any given time. The proposed principle of interaction between the elements of a construct (X, Y, S1, S2, M) is universal and applies for all modes of communication (normal, deaf, blind, deaf and blind people) and for various language systems (Chinese, Italian, English, etc.). The particular arrangement of modalities of each of the three modules S1 (5 of 5), S2 (1 of 3), and M (3 of 3) defines a specific mode of communication. This multifaceted paradigm demonstrates a predetermined pattern of relationships between X, Y, and M that passes from generation to generation. The presented analysis of a cognitive experience encompasses the key elements of embodied cognition theories and unequivocally accords with the scientific interpretation of cognition as the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses, and cognition means thinking and awareness. By assembling the novel ideas presented in twelve sections, we can reveal that in the invisible “chaos”, there is an order, a structure with landmarks and principles of operations and mental processes (thoughts) are physical and have a biological basis. This innovative proposal explains the phenomenon of mental imagery; give the first insight into the relationship between mental states and brain states, and support the notion that mind and body are inseparably connected. The findings of this theoretical proposal are supported by the current scientific data and are substantiated by the records of the evolution of language and human intelligence.

Keywords: agent, awareness, cognitive, element, experience, feedback, first person, imagery, language, mental, motor, object, sensory, symbol, thought

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Authors: Roman Matejka, Vaclav Prochazka, Tibor Izak, Jana Stepanovska, Martina Travnickova, Alexander Kromka

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Cell-based impedance spectroscopy is suitable method for electrical monitoring of cell activity especially on substrates that cannot be easily inspected by optical microscope (without fluorescent markers) like decellularized tissues, nano-fibrous scaffold etc. Special sensor for this measurement was developed. This sensor consists of corning glass substrate with gold interdigitated electrodes covered with diamond layer. This diamond layer provides biocompatible non-conductive surface for cells. Also, a special PPFC flow cultivation chamber was developed. This chamber is able to fix sensor in place. The spring contacts are connecting sensor pads with external measuring device. Construction allows real-time live cell imaging. Combining with perfusion system allows medium circulation and generating shear stress stimulation. Experimental evaluation consist of several setups, including pure sensor without any coating and also collagen and fibrin coating was done. The Adipose derived stem cells (ASC) and Human umbilical vein endothelial cells (HUVEC) were seeded onto sensor in cultivation chamber. Then the chamber was installed into microscope system for live-cell imaging. The impedance measurement was utilized by vector impedance analyzer. The measured range was from 10 Hz to 40 kHz. These impedance measurements were correlated with live-cell microscopic imaging and immunofluorescent staining. Data analysis of measured signals showed response to cell adhesion of substrates, their proliferation and also change after shear stress stimulation which are important parameters during cultivation. Further experiments plan to use decellularized tissue as scaffold fixed on sensor. This kind of impedance sensor can provide feedback about cell culture conditions on opaque surfaces and scaffolds that can be used in tissue engineering in development artificial prostheses. This work was supported by the Ministry of Health, grants No. 15-29153A and 15-33018A.

Keywords: bio-impedance measuring, bioreactor, cell cultivation, diamond layer, gold interdigitated electrodes, tissue engineering

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Authors: Turker Yardan, Bahattin Avci, S. Sirri Bilge, Ayhan Bozkurt

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Sinapic acid (SA) is a phenylpropanoid compound with anti-inflammatory, antioxidant, and neuroprotective activities. The purpose of this study was to characterize the possible protective effect of sinapic acid on chlorpyrifos (CPF), a common organophosphorus pesticide used worldwide, induced toxicity in rats. Forty male and female rats (240-270 g) were used in this study. Each group was composed of 5 male and 5 female rats. Sinapic acid (20 mg/kg or 40 mg/kg) or vehicle (olive oil, 1 ml ⁄ rat) were given orally for 5 days. CPF (279 mg/kg) or vehicle (peanut oil, 2 ml ⁄ kg, s.c.) was administered on the sixth day, immediately after the recording of the body weight of the animals. Twenty four hours following CPF administration body weight, body temperature and locomotor activity values were recorded before decapitation of the animals. Trunk blood, brain, and liver samples were collected for biochemical examinations. Chlorpyrifos administration decreased butyrylcholinesterase activity in blood, brain, and liver, while it increased malondialdehyde (MDA) levels and advanced oxidation protein products (AOPPs) (p < 0.01 - 0.001). Additionally, CPF administration reduced the body weight, body temperature, and locomotor activity values of the animals (p < 0.01 - 0.001). All these physiological and biochemical changes induced by CPF were reduced with the 40 mg/kg dose of SA (p < 0.05 - 0.001). Our results suggest that SA administration ameliorates CPF induced toxicity in rats, possibly by supporting the antioxidant mechanism.

Keywords: antioxidant, Chlorpyrifos, poisoning, sinapic acid

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Authors: Huafeng Wei

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Failures of developing new drugs primarily based on the amyloid pathology hypothesis after decades of efforts internationally lead to changes of focus targeting alternative pathways of pathology in Alzheimer’s disease (AD). Disruption of intracellular Ca2+ homeostasis, especially the pathological and excessive Ca2+ release from the endoplasmic reticulum (ER) via ryanodine receptor (RyRs) Ca2+ channels, has been considered an upstream pathology resulting in major AD pathologies, such as amyloid and Tau pathology, mitochondria damage and inflammation, etc. Therefore, dantrolene, an inhibitor of RyRs that reduces the pathological Ca2+ release from ER and a clinically available drug for the treatment of malignant hyperthermia and muscle spasm, is expected to ameliorate AD multiple pathologies synapse and cognitive dysfunction. Our own studies indicated that dantrolene ameliorated impairment of neurogenesis and synaptogenesis in neurons developed from induced pluripotent stem cells (iPSCs) originated from skin fibroblasts of either familiar (FAD) or sporadic (SAD) AD by restoring intracellular Ca2+ homeostasis. Intranasal administration of dantrolene significantly increased its passage across the blood-brain barrier (BBB) and, therefore its brain concentrations and durations. This can render dantrolene a more effective therapeutic drug with fewer side effects for chronic AD treatment. This review summarizes the potential therapeutic and side effects of dantrolene and repurposes intranasal dantrolene as a disease-modifying drug for future AD treatment.

Keywords: Alzheimer's disease, calcium, drug development, dementia, neurodegeneration, neurogenesis

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Authors: Francesca La Rosa , Marina Saresella, Mario Clerici, Michael Heneka

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Neuroinflammation plays a key role in the modulation of the pathogenesis of neurodegenerative disorder such as Alzheimer's Disease (AD). Microglia, the main immune effector of the brain, are able to migrate to sites of Amyloid-beta (Aβ) deposition to eliminate Aβ phagocytosis upon activation by multiple receptors: Toll like receptors and scavenger receptors. The issue of whether microglia are able to eliminate pathological lesions such as neurofibrillary tangles or senile plaques from AD brain still remains the matter of controversy. Recent data suggest that the Nod Like Receptor 3 (NLRP3), multiprotein inflammasome complexes, plays a role in AD, as its activation in the microglia by Aβ triggers. IL-1β is produced as a biologically inactive pro-form and requires caspase-1 for activation and secretion. Caspase-1 activity is controlled by inflammasomes. We investigate about the importance of inflammasomes complex in the Aβ phagocytosis and its degradation. The preliminary results of phagocytosis assay and immunofluorescent experiment on primary Microglia cells to lipopolysaccharide (LPS) an Aβ exposure show that a previous treatment with LPS reduce Aβ phagocytosis. Different results were obtained in Primary Microglia wild type, NLRP3 and ASC Knockout suggesting a real inflammasomes involvement in Alzheimer's pathology. Inflammasomes inactivation reduces the production of inflammatory cytokines prolonging the protective activity of microglia and Aβ clearance, featuring a typical microglia phenotype of the early stage of AD disease.

Keywords: Alzheimer disease, innate immunity, neuroinflammation, NLRP3

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Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

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Authors: Khaled M. Ali, Ayman A. Mostafa, Soliman M. Soliman

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Objectives: To describe the most common clinical and endoscopic findings associated with complicated corneal ulcers in cats, and to determine the short-term outcomes after surgical treatment of these cats. Animals Eighteen client-owned cats of different breeds (52 females and 28 males), ranging in age from 3 months to 6 years, with corneal ulcers. Procedures: Cats were clinically evaluated to initially determine the concurrent corneal abnormalities. Endoscopic examination was performed to determine the anterior and posterior segments abnormalities. Superficial and deep stromal ulcers were treated using conjunctival flap. Corneal sequestrum was treated by partial keratectomy and conjunctival flap. Anterior synechia was treated via peripheral iridectomy and separation of the adhesion between the iris and the inner cornea. Symblepharon was treated by removal of the adhered conjunctival membrane from the cornea. Incurable endophthalmitis was treated surgically by extirpation. Short-term outcomes after surgical managements of selected corneal abnormalities were then assessed clinically and endoscopically. Results: Deep stromal ulcer with descemetocele, endophthalmitis, symblepharon, corneal sequestration and anterior synechia with secondary glaucoma and corneal scarring were the most common complications of corneal ulcer. FHV-1 was a common etiologic factor of corneal ulceration. Persistent corneal scars of varying shape and size developed in cats with deep stromal ulcer, anterior synechia, and corneal sequestration. Conclusions: Domestic shorthaired and Persian cats were the most predisposed breeds to FHV-1 infection and subsequent corneal ulceration. Immediate management of patients with corneal ulcer would prevent serious complications. No age or sex predisposition to complicated corneal ulceration in cats.

Keywords: cats, complicated corneal ulceration, clinical, endoscopic diagnosis, FHV-1

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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Authors: Mariane G. Tadros, Shahira M. Ezzat, Maha M. Salama, Mohamed A. Farag

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In this study, the in vitro anticholinesterase activity of the volatile oils of both O. basilicum and O. africanum was investigated and both samples showed significant activity. As a result, the major constituents of the two oils were isolated using several column chromatography. Linalool, 1,8-cineol and eugenol were isolated from the volatile oil of O. basilicum and camphor was isolated from the volatile oil of O. africanum. The anticholinesterase activity of the isolated compounds were also evaluated where 1,8-cineol showed the highest inhibitory activity followed by camphor. To confirm these activities, learning and memory enhancing effects were tested in mice. Memory impairment was induced by scopolamine, a cholinergic muscarinic receptor antagonist. Anti-amnesic effects of both volatile oils and their terpenoids were investigated by the passive avoidance task in mice. We also examined their effects on brain acetylcholinesterase activity. Results showed that scopolamine-induced cognitive dysfunction was significantly attenuated by administration of the volatile oils and their terpenoids, eugenol and camphor, in the passive avoidance task and inhibited brain acetylcholinesterase activity. These results suggest that O. basilicum and O. africanum volatile oils can be good candidates for further studies on Alzheimer’s disease via their acetylcholinesterase inhibitory actions.

Keywords: Ocimum baselicum, Ocimum africanum, GC/MS analysis, anticholinesterase

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Authors: Meysam Lak, Mohammad Fatehi Marji, Alireza Yarahamdi Bafghi, Abolfazl Abdollahipour

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A wellbore is a hole that is drilled to aid in the exploration and recovery of natural resources including oil and gas. Occasionally, in order to increase productivity index and porosity of the wellbore and reservoir, the well stimulation methods have been used. Hydraulic fracturing is one of these methods. Moreover, several explosions at the end of the well can stimulate the reservoir and create fractures around it. In this study, crack initiation in rock around the wellbore has been numerically modeled due to explosion. One, two, three, and four pairs of explosion have been set at the end of the wellbore on its wall. After each stage of the explosion, results have been presented and discussed. Results show that this method can initiate and probably propagate several fractures around the wellbore.

Keywords: crack initiation, explosion, finite difference modelling, well productivity

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Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon

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Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.

Keywords: deep-learning, altimetry, sea surface temperature, forecast

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Authors: Kouassi Louis Kra

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The nearshore deep-water gravity flow deposits in the Northern steep slope of Dongying depression, Bohai Bay basin, have been acknowledged as important reservoirs in the rift lacustrine basin. These deep strata term as coarse clastic sediment, deposit at the root of the slope have complex depositional processes and involve wide diagenetic events which made high-quality reservoir prediction to be complex. Based on the integrated study of seismic interpretation, sedimentary analysis, petrography, cores samples, wireline logging data, 3D seismic and lithological data, the reservoir formation mechanism deciphered. The Geoframe software was used to analyze 3-D seismic data to interpret the stratigraphy and build a sequence stratigraphic framework. Thin section identification, point counts were performed to assess the reservoir characteristics. The software PetroMod 1D of Schlumberger was utilized for the simulation of burial history. CL and SEM analysis were performed to reveal diagenesis sequences. Backscattered electron (BSE) images were recorded for definition of the textural relationships between diagenetic phases. The result showed that the nearshore steep slope deposits mainly consist of conglomerate, gravel sandstone, pebbly sandstone and fine sandstone interbedded with mudstone. The reservoir is characterized by low-porosity and ultra-low permeability. The diagenesis reactions include compaction, precipitation of calcite, dolomite, kaolinite, quartz cement and dissolution of feldspars and rock fragment. The main types of reservoir space are primary intergranular pores, residual intergranular pores, intergranular dissolved pores, intergranular dissolved pores, and fractures. There are three obvious anomalous high-porosity zones in the reservoir. Overpressure and early hydrocarbon filling are the main reason for abnormal secondary pores development. Sedimentary facies control the formation of high-quality reservoir, oil and gas filling preserves secondary pores from late carbonate cementation.

Keywords: Bohai Bay, Dongying Sag, deep strata, formation mechanism, high-quality reservoir

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Authors: Selina Doncevic, Lisa Perla, Angela Kindvall

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The Global War on Terror which began after September 11, 2011, increased survivability of severe injuries requiring varying trajectories of rehabilitation and recovery. The costs encompass physiologic, functional, social, emotional, psychological, vocational and scholastic domains of life. The purpose of this poster is to inform private sector health care practitioners and clinicians at various levels of the unique and long term dynamics of healthcare recovery for polytrauma, and traumatic brain injured service members and veterans in the United States of America. Challenges include care delivery between the private sector, the department of defense, and veterans affairs healthcare systems while simultaneously supporting the dynamics of acute as well as latent complications associated with severe injury and illness. Clinical relevance, subtleties of protracted recovery, and overwhelmed systems of care are discussed in the context of lessons learned and in reflection on previous wars. Additional concerns for consideration and discussion include: the cost of protracted healthcare, various U.S. healthcare payer systems, lingering community reintegration challenges, ongoing care giver support, the rise of veterans support groups and the development of private sector clinical partnerships.

Keywords: brain injury, future, polytrauma, rehabilitation

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Authors: Hai Zhou, Wenzhi Zhao

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Water is one of the most important factors limiting plant growth and development in desert ecosystems. In order to understand how desert shrubs cope with variation in water sources over time, it is important to understand plant–water relations in desert-oasis ecotone. We selected the typical desert shrubs: Nitraria sibirica, Calligonum mongolicum and Haloxylon ammodendron of 5-, 10-, 20- and 40-year old as the research species, to study the seasonal variation of plant water sources and response to precipitation in the desert-oasis ecotone of Linze, Northwestern China. We examined stable isotopic ratios of oxygen (δ18O) in stem water of desert shrubs as well as in precipitation, groundwater, and soil water in different soil layers and seasons to determine water sources for the shrubs. We found that the N. sibirica and H. ammodendron of 5-, 10-year old showed significant seasonal variation characteristics of δ18O value of stem water and water sources. However, the C. mongolicum and 20- and 40-year H. ammodendron main water sources were from deep soil water and groundwater, and less response to precipitation pulse. After 22.4 mm precipitation, the contribution of shallow soil water (0-50cm) to the use of N. sibirica increased from 6.7% to 36.5%; the C. mongolicum rarely use precipitation that were about 58.29% and 23.51%, absorbed from the deep soil water and groundwater; the contribution of precipitation to use of H. ammodendron had significantly differences among the four ages. The H. ammodendron of 5- and 10-year old about 86.3% and 42.5% water sources absorbed from the shallow soil water after precipitation. However, the contribution to 20- and 40-year old plant was less than 15%. So, the precipitation was one of the main water sources for desert shrubs, but the species showed different water utilization. We conclude that the main water source of the N. sibirica and H. ammodendron of 5-, 10-year was soil water recharged by precipitation, but the deeply rooted H. ammodendron of 20‐ and 40‐year‐old and the C. mongolicum have the ability to exploit a deep and reliable water source.

Keywords: water use pattern, water resource, stable isotope, seasonal change, precipitation pulse

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Yung-Chih Kuo, Yung-I Lou

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Cationic solid lipid nanoparticles (CSLNs) with anti-melanotransferrin (AMT) and apolipoprotein E (ApoE) were used to carry antimitotic doxorubicin (Dox) across the blood–brain barrier (BBB) for glioblastoma multiforme (GBM) treatment. Dox-loaded CSLNs were prepared in microemulsion, grafted covalently with AMT and ApoE, and applied to human brain microvascular endothelial cells (HBMECs), human astrocytes, and U87MG cells. Experimental results revealed that an increase in the weight percentage of stearyl amine (SA) from 0% to 20% increased the size of AMT-ApoE-Dox-CSLNs. In addition, an increase in the stirring rate from 150 rpm to 450 rpm decreased the size of AMT-ApoE-Dox-CSLNs. An increase in the weight percentage of SA from 0% to 20% enhanced the zeta potential of AMT-ApoE-Dox-CSLNs. Moreover, an increase in the stirring rate from 150 rpm to 450 rpm reduced the zeta potential of AMT-ApoE-Dox-CSLNs. AMT-ApoE-Dox-CSLNs exhibited a spheroid-like geometry, a minor irregular boundary deviating from spheroid, and a somewhat distorted surface with a few zigzags and sharp angles. The encapsulation efficiency of Dox in CSLNs decreased with increasing weight percentage of Dox and the order in the encapsulation efficiency of Dox was 10% SA > 20% SA > 0% SA. However, the reverse order was true for the release rate of Dox, suggesting that AMT-ApoE-Dox-CSLNs containing 10% SA had better-sustained release characteristics. An increase in the concentration of AMT from 2.5 to 7.5 μg/mL slightly decreased the grafting efficiency of AMT and an increase in that from 7.5 to 10 μg/mL significantly decreased the grafting efficiency. Furthermore, an increase in the concentration of ApoE from 2.5 to 5 μg/mL slightly reduced the grafting efficiency of ApoE and an increase in that from 5 to 10 μg/mL significantly reduced the grafting efficiency. Also, AMT-ApoE-Dox-CSLNs at 10 μg/mL of ApoE could slightly reduce the transendothelial electrical resistance (TEER) and increase the permeability of propidium iodide (PI). An incorporation of 10 μg/mL of ApoE could reduce the TEER and increase the permeability of PI. AMT-ApoE-Dox-CSLNs at 10 μg/mL of AMT and 5-10 μg/mL of ApoE could significantly enhance the permeability of Dox across the BBB. AMT-ApoE-Dox-CSLNs did not induce serious cytotoxicity to HBMECs. The viability of HBMECs was in the following order: AMT-ApoE-Dox-CSLNs = AMT-Dox-CSLNs = Dox-CSLNs > Dox. The order in the efficacy of inhibiting U87MG cells was AMT-ApoE-Dox-CSLNs > AMT-Dox-CSLNs > Dox-CSLNs > Dox. A surface modification of AMT and ApoE could promote the delivery of AMT-ApoE-Dox-CSLNs to cross the BBB via melanotransferrin and low density lipoprotein receptor. Thus, AMT-ApoE-Dox-CSLNs have appropriate physicochemical properties and can be a potential colloidal delivery system for brain tumor chemotherapy.

Keywords: anti-melanotransferrin, apolipoprotein E, cationic catanionic solid lipid nanoparticle, doxorubicin, U87MG cells

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Authors: A. Bruce Janati, Muhammad Umair Khan, Naif Alghassab, Ibrahim Alzeir, Assem Mahmoud, M. Sammour

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Rationale: Lennox-Gastaut syndrome(LGS) is an electro-clinical syndrome composed of the triad of mental retardation, multiple seizure types, and the characteristic generalized slow spike-wave complexes in the EEG. In this article, we report on two patients with LGS whose brain MRI showed dysgenesis of corpus callosum(CC). We review the literature and stress the role of CC in the genesis of secondary bilateral synchrony(SBS). Method: This was a clinical study conducted at King Khalid Hospital. Results: The EEG was consistent with LGS in patient 1 and unilateral slow spike-wave complexes in patient 2. The MRI showed hypoplasia of the splenium of CC in patient 1, and global hypoplasia of CC combined with Joubert syndrome in patient 2. Conclusion: Based on the data, we proffer the following hypotheses: 1-Hypoplasia of CC interferes with functional integrity of this structure. 2-The genu of CC plays a pivotal role in the genesis of secondary bilateral synchrony. 3-Electrodecremental seizures in LGS emanate from pacemakers generated in the brain stem, in particular the mesencephalon projecting abnormal signals to the cortex via thalamic nuclei. 4-Unilateral slow spike-wave complexes in the context of mental retardation and multiple seizure types may represent a variant of LGS, justifying neuroimaging studies.

Keywords: EEG, Lennox-Gastaut syndrome, corpus callosum , MRI

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Authors: Vera G. Dianova, Lori P. Montross, Charles M. Burke

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In the digital age, the widespread and frequently excessive use of mobile phones amongst university students is recognized as a significant distractor which interferes with their ability to enter a deep state of concentration during studies and diminishes their prospects of experiencing the enjoyable and instrumental state of flow, as defined and described by psychologist M. Csikszentmihalyi. This study has targeted 50 university students with the aim of teaching them to cultivate their ability to engage in deep work and to attain the state of flow, fostering more effective and enjoyable learning experiences. Prior to the start of the intervention, all participating students completed a comprehensive survey based on a variety of validated scales assessing their inclination toward lifelong learning, frequency of flow experiences during study, frustration tolerance, sense of agency, as well as their love of learning and daily time devoted to non-academic mobile phone activities. Several days after this initial assessment, students received a 90-minute lecture on the principles of flow and deep work, accompanied by a critical discourse on the detrimental effects of excessive mobile phone usage. They were encouraged to practice deep work and strive for frequent flow states throughout the semester. Subsequently, students submitted weekly surveys, including the 10-item CORE Dispositional Flow Scale, a 3-item agency scale and furthermore disclosed their average daily hours spent on non-academic mobile phone usage. As a final step, at the end of the semester students engaged in reflective report writing, sharing their experiences and evaluating the intervention's effectiveness. They considered alterations in their love of learning, reflected on the implications of their mobile phone usage, contemplated improvements in their tolerance for boredom and perseverance in complex tasks, and pondered the concept of lifelong learning. Additionally, students assessed whether they actively took steps towards managing their recreational phone usage and towards improving their commitment to becoming lifelong learners. Employing a mixed-methods approach our study offers insights into the dynamics of concentration, flow, mobile phone usage and attitudes towards learning among undergraduate and graduate university students. The findings of this study aim to promote profound contemplation, on the part of both students and instructors, on the rapidly evolving digital-age higher education environment. In an era defined by digital and AI advancements, the ability to concentrate, to experience the state of flow, and to love learning has never been more crucial. This study underscores the significance of addressing mobile phone distractions and providing strategies for cultivating deep concentration. The insights gained can guide educators in shaping effective learning strategies for the digital age. By nurturing a love for learning and encouraging lifelong learning, educational institutions can better prepare students for a rapidly changing labor market, where adaptability and continuous learning are paramount for success in a dynamic career landscape.

Keywords: deep work, flow, higher education, lifelong learning, love of learning

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Authors: Zhifeng Kong

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Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.

Keywords: over-parameterization, rectified linear units ReLU, convergence, gradient descent, neural networks

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Authors: Cláudia Patrocínio, Beatriz Fernandes, Ana Filipa Pires

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Background: Mirror therapy (MT) is used to improve motor function after stroke. During MT, a mirror is placed between the two upper limbs (UL), thus reflecting movements of the non- affected side as if it were the affected side. Objectives: The aim of this review is to analyze the evidence on the effec.tiveness of MT in the recovery of UL function in population with post chronic stroke. Methods: The literature search was carried out in PubMed, ISI Web of Science, and PEDro database. Inclusion criteria: a) studies that include individuals diagnosed with stroke for at least 6 months; b) intervention with MT in UL or comparing it with other interventions; c) articles published until 2023; d) articles published in English or Portuguese; e) randomized controlled studies. Exclusion criteria: a) animal studies; b) studies that do not provide a detailed description of the intervention; c) Studies using central electrical stimulation. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database (PEDro) scale. Studies with < 4 on PEDro scale were excluded. Eighteen studies met all the inclusion criteria. Main results and conclusions: The quality of the studies varies between 5 and 8. One article compared muscular strength training (MST) with MT vs without MT and four articles compared the use of MT vs conventional therapy (CT), one study compared extracorporeal shock therapy (EST) with and without MT and another study compared functional electrical stimulation (FES), MT and biofeedback, three studies compared MT with Mesh Glove (MG) or Sham Therapy, five articles compared performing bimanual exercises with and without MT and three studies compared MT with virtual reality (VR) or robot training (RT). The assessment of changes in function and structure (International Classification of Functioning, Disability and Health parameter) was carried out, in each article, mainly using the Fugl Meyer Assessment-Upper Limb scale, activity and participation (International Classification of Functioning, Disability and Health parameter) were evaluated using different scales, in each study. The positive results were seen in these parameters, globally. Results suggest that MT is more effective than other therapies in motor recovery and function of the affected UL, than these techniques alone, although the results have been modest in most of the included studies. There is also a more significant improvement in the distal movements of the affected hand than in the rest of the UL.

Keywords: physical therapy, mirror therapy, chronic stroke, upper limb, hemiplegia

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Authors: Saleh Mohamed, Mohamed El-Shal, Taha Kumosani, Ahmad Mal, Youssri Ahmed, Yasser Almulaiky

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The marine environment of the Jeddah southern red sea shore is subjected to increasing anthropogenic activities as sewage sludge draining and desalting processes. The objective of this study is to compare the quantitative responses of enzymatic biomarkers in fish from polluted area with the responses of organism from reference area. Enzymatic biomarkers as neurotoxic, antioxidant and detoxifying enzymes were evaluated in the brain and liver from Variola louti as a sentinel species sampled from both polluted and reference sites in the Jeddah southern red sea shore during four months January, April, July and October in 2014 and 2015. In brain of V. louti, the activity of acetylcholinestease (AChE) collected from reference area significantly increased 8.8 and 10.5 folds than that from polluted area in 2014 and 2015, respectively. The activities of catalase (CAT), glutathione reductase (GR) and glutathione peroxidase (GPx) and glutathione-S-transferase (GST) from liver of V. louti in polluted area significantly increased 1.4, 1.27 and 3, 4.5 and 4.37, 2 and 5, 4.5 folds than that from reference area in 2014 and 2015, respectively. The levels of examined enzymes are approximately similar in the four seasons detected in 2014 and 2015 indicating that the similar components of sewage were draining in red sea. In conclusion, these findings suggest the important of enzymatic biomarkers in monitoring the pollution in Jeddah red sea shore.

Keywords: Variola louti, enzymatic biomarkers, pollution, Red sea

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Authors: Jack Nash, Chris Simpson, Holly Hurn, Ronel Terblanche, Alan Mistlin

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There is an increasing incidence of mild traumatic brain injury (mTBI) cases throughout sport and with this, a growing interest from governing bodies to ensure these are managed appropriately and player welfare is prioritised. The Berlin consensus statement on concussion in sport recommends a multidisciplinary approach when managing those patients who do not have full resolution of mTBI symptoms. There are as of yet no standardised guideline to follow in the treatment of complex cases mTBI in athletes. The aim of this project was to analyse the outcomes, both clinical and vocational, of all patients admitted to the mild Traumatic Brain Injury (mTBI) service at the UK’s Defence Military Rehabilitation Centre Headley Court between 1st June 2008 and 1st February 2017, as a result of a sport induced injury, and evaluate potential predictive indicators of outcome. Patients were identified from a database maintained by the mTBI service. Clinical and occupational outcomes were ascertained from medical and occupational employment records, recorded prospectively, at time of discharge from the mTBI service. Outcomes were graded based on the vocational independence scale (VIS) and clinical documentation at discharge. Predictive indicators including referral time, age at time of injury, previous mental health diagnosis and a financial claim in place at time of entry to service were assessed using logistic regression. 45 Patients were treated for sport-related mTBI during this time frame. Clinically 96% of patients had full resolution of their mTBI symptoms after input from the mTBI service. 51% of patients returned to work at their previous vocational level, 4% had ongoing mTBI symptoms, 22% had ongoing physical rehabilitation needs, 11% required mental health input and 11% required further vestibular rehabilitation. Neither age, time to referral, pre-existing mental health condition nor compensation seeking had a significant impact on either vocational or clinical outcome in this population. The vast majority of patients reviewed in the mTBI clinic had persistent symptoms which could not be managed in primary care. A consultant-led, multidisciplinary approach to the diagnosis and management of mTBI has resulted in excellent clinical outcomes in these complex cases. High levels of symptom resolution suggest that this referral and treatment pathway is successful and is a model which could be replicated in other organisations with consultant led input. Further understanding of both predictive and individual factors would allow clinicians to focus treatments on those who are most likely to develop long-term complications following mTBI. A consultant-led, multidisciplinary service ensures a large number of patients will have complete resolution of mTBI symptoms after sport-related mTBI. Further research is now required to ascertain the key predictive indicators of outcome following sport-related mTBI.

Keywords: brain injury, concussion, neurology, rehabilitation, sports injury

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Authors: Ihab Elaff

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Simulating the heart’s electrical stimulation is essential in modeling and evaluating the electrophysiology behavior of the heart. For achieving that, there are two structures in concern: the ventricles’ Myocardium, and the ventricles’ Conduction Network. Ventricles’ Myocardium has been modeled as anisotropic material from Diffusion Tensor Imaging (DTI) scan, and the Conduction Network has been extracted from DTI as a case-based structure based on the biological properties of the heart tissues and the working methodology of the Magnetic Resonance Imaging (MRI) scanner. Results of the produced activation were much similar to real measurements of the reference model that was presented in the literature.

Keywords: diffusion tensor, DTI, heart, conduction network, excitation propagation

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Shin Teng, Yu-Te Wu, Sui-Hing Yan

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Brain functional networks based on resting-state EEG data were compared between patients with mild Alzheimer’s disease (mAD) and matched patients with amnestic subtype of mild cognitive impairment (aMCI). We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions and the network analysis based on graph theory to further investigate the alterations of functional networks in mAD compared with aMCI group. We aimed at investigating the changes of network integrity, local clustering, information processing efficiency, and fault tolerance in mAD brain networks for different frequency bands based on several topological properties, including degree, strength, clustering coefficient, shortest path length, and efficiency. Results showed that the disruptions of network integrity and reductions of network efficiency in mAD characterized by lower degree, decreased clustering coefficient, higher shortest path length, and reduced global and local efficiencies in the delta, theta, beta2, and gamma bands were evident. The significant changes in network organization can be used in assisting discrimination of mAD from aMCI in clinical.

Keywords: EEG, functional connectivity, graph theory, TFCMI

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Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

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Authors: Hasmik V. Zanginyan, Gayane S. Ghazaryan, Laura M. Hovsepyan

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Experimental autoimmune encephalomyelitis (EAE) is an inflammatory demyelinating disease of the central nervous system that is induced in laboratory animals by developing an immune response against myelin epitopes. The typical clinical course is ascending palsy, which correlates with inflammation and tissue damage in the thoracolumbar spinal cord, although the optic nerves and brain (especially the subpial white matter and brainstem) are also often affected. With multiple sclerosis, there is a violation of lipid metabolism in myelin. When membrane lipids (glycosphingolipids, phospholipids) are disturbed, metabolites not only play a structural role in membranes but are also sources of secondary mediators that transmit multiple cellular signals. The purpose of this study was to investigate the effect of ganglioside as a therapeutic agent in experimental multiple sclerosis. The biological activity of a ganglioside-containing medicinal preparation (Cronassial) was evaluated in an experimental model of multiple sclerosis in laboratory animals. An experimental model of multiple sclerosis in rats was obtained by immunization with myelin basic protein (MBP), as well as homogenization of the spinal cord or brain. EAE was induced by administering a mixture of an encephalitogenic mixture (EGM) with Complete Freund’s Adjuvant. Mitochondrial fraction was isolated in a medium containing 0,25 M saccharose and 0, 01 M tris buffer, pH - 7,4, by a method of differential centrifugation on a K-24 centrifuge. Glutathione peroxidase activity was assessed by reduction reactions of hydrogen peroxide (H₂O₂) and lipid hydroperoxides (ROOH) in the presence of GSH. LPO activity was assessed by the amount of malondialdehyde (MDA) in the total homogenate and mitochondrial fraction of the spinal cord and brain of control and experimental autoimmune encephalomyelitis rats. MDA was assessed by a reaction with Thiobarbituric acid. For statistical data analysis on PNP, SPSS (Statistical Package for Social Science) package was used. The nature of the distribution of the obtained data was determined by the Kolmogorov-Smirnov criterion. The comparative analysis was performed using a nonparametric Mann-Whitney test. The differences were statistically significant when р ≤ 0,05 or р ≤ 0,01. Correlational analysis was conducted using a nonparametric Spearman test. In the work, refrigeratory centrifuge, spectrophotometer LKB Biochrom ULTROSPECII (Sweden), pH-meter PL-600 mrc (Israel), guanosine, and ATP (Sigma). The study of the process of lipid peroxidation in the total homogenate of the brain and spinal cord in experimental animals revealed an increase in the content of malonic dialdehyde. When applied, Cronassial observed normalization of lipid peroxidation processes. Reactive oxygen species, causing lipid peroxidation processes, can be toxic both for neurons and for oligodendrocytes that form myelin, causing a violation of their lipid composition. The high content of lipids in the brain and the uniqueness of their structure determines the nature of the development of LPO processes. The lipid layer of cellular and intracellular membranes performs two main functions -barrier and matrix (structural). Damage to the barrier leads to dysregulation of intracellular processes and severe disorders of cellular functions.

Keywords: experimental autoimmune encephalomyelitis, multiple sclerosis, neuroinflammation, therapy

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Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

Procedia PDF Downloads 248