Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Spinal Anesthesia on Postoperative Cognitive Dysfunction

Background: It seems that spinal anesthesia is an appropriate approach for elderly patients and those who are not suitable candidates for general anesthesia. This study aimed to determine the effect of spinal anesthesia on postoperative cognitive decline in elderly patients. Methods: This cross-sectional study was performed on 81 patients older than 65 years of age with the ASA classes I and II. The cases were candidates for surgery and spinal anesthesia and referred to Besat Hospital, Hamadan, Iran. Moreover, they receive surgical treatment and underwent spinal anesthesia from 2019 to 2020. Wechsler Memory Scale-III (WMS-III) was utilized to compare the cognitive dysfunction in elderly patients before and after spinal anesthesia. Furthermore, the WMS-III scores were compared before and after anesthesia to assess the severity of cognitive dysfunction, short- and long-term memories, as well as concentration rate at discharge. Results: According to the results, there was no significant difference before and after spinal anesthesia regarding the mean mental control, repetition, visual reproduction, and verbal paired associates (P>0.05). Moreover, the mean values of spatial addition (Z=-2.07; P=0.03) navigation (Z=-2.13; P=0.03), and logical memory (Z=-2.42; P=0.01) increased after spinal anesthesia. In addition, the mean values of the memory coefficient were estimated at 68.28±16.77 and 68.66±16.94, respectively. The comparison of memory coefficient among elderly patients undergoing spinal anesthesia showed no significant difference before and after spinal anesthesia (Z=-1.51; P=0.13). A reverse correlation was reported between age and memory coefficient before (r=-0.46; P<0.005) and after spinal anesthesia (r=-0.405; P<0.005). Conclusion: Spinal anesthesia had no effects on memory coefficient. However, there was an increase in some memory function domains, including spatial addition, navigation, and logical memory after the operation. The memory scores may have been affected by the patients' conditions before the operation.
Garvicins AG1 and AG2: Two Novel Class IId Bacteriocins of Lactococcus garvieae Lg-Granada

Lactococcus garvieae causes infectious diseases in animals and is considered an emerging zoonotic pathogen involved in human clinical conditions. In silico analysis of plasmid pLG50 of L. garvieae Lg-Granada, an isolate from a patient with endocarditis, revealed the presence of two gene clusters (orf46–47 and orf48–49), each one encoding a novel putative bacteriocin, i.e., garvicin AG1 (GarAG1; orf46) and garvicin AG2 (GarAG2; orf48), and their corresponding immunity proteins (orf47 and orf49). The chemically synthesised bacteriocins GarAG1 and GarAG2 presented inhibitory activity against pathogenic L. garvieae strains, with AG2 also being active against Listeria monocytogenes, Listeria ivanovii and Enterococcus faecalis. Genetic organisation, amino acid sequences and antimicrobial activities of GarAG1 and GarAG2 indicate that they belong to linear non-pediocin-like one-peptide class IId bacteriocins. Gram-positive bacteria that were sensitive to GarAG2 were also able to ferment mannose, suggesting that this bacteriocin could use the mannose phosphotransferase transport system (Man-PTS) involved in mannose uptake as a receptor in sensitive strains. Intriguingly, GarAG1 and GarAG2 were highly active against their own host, L. garvieae Lg-Granada, which could be envisaged as a new strategy to combat pathogens via their own weapons.
Coronary disease prediction by using upgraded deep learning CNN

The determination of coronary failure has transformed into troublesome analytic effort in the present analytical examination. This finding turn to the point-by-point and accurate examination of the victim’s analytical facts on a single health report. The tremendous improvements in occupied deep literacy look to construct robotized structure which aid expert the couple to foresee and identify the weakness with the internet of things (IoT) help. In this way, the magnify machine learning by neural networks helped Convolutional Neural Network has been build to help and work on persistent forecast of heart disease. The Upgraded Deep CNN model is concentrated throughout deep plan that occupy multi-facet perceptron's model with training about normalization draws near. Besides, the structured implementation is accepted with full elements and limited high points. Henceforth, the reduced in the high points influences the fertility divides as far as pick up beat, and precision has been differentially examined with concluded outcomes. The Upgraded Deep CNN structure one time carried out on the Internet of Medical Things Platform for option inner concerned webs, which assists experts with successfully diagnosing cardiac sufferers information in auxiliary storage all over the globe.
Tunable interdigital transducers made of piezoelectric macro-fiber composite

The number of applications of Lamb waves (LWs) based structural health monitoring (SHM) has significantly increased in recent decades. The growth of interest results from several advantages of this diagnostic technique, that is, considerable mode selectivity and directivity that allow for the assessment of the technical condition of a monitored structure. Successful applications of LWs in the field of SHM stimulate continuous improvement of the transducers’ design to enable capturing more reliable diagnostic data. The paper introduces a new type of transducer that may be used in the LWs based SHM systems, namely tunable-interdigital transducer (T-IDT) based on macro-fiber composites (MFC). The authors provide a short overview on different types of transducers that may be used in SHM applications, followed by a detailed description of the structure of proposed T-IDT. Finally, the results of numerical and experimental tests carried out employing the proposed transducer are discussed and compared to those obtained with a traditional IDT.
Optimization of Regional Logistics Distribution Information Network Platform

In order to solve the problem of regional logistics distribution, a study on the optimization of regional logistics distribution information network platform is proposed. Firstly, in order to meet the development requirements of regional logistics and improve customer satisfaction, the regional logistics path is optimized to solve the problem of long time consumption for the optimization algorithm of regional logistics distribution path to find the optimal path. The clustering algorithm is used to divide the logistics distribution area, and the multiobjective function of logistics distribution path optimization is established, mainly including weight index, timeliness index, customer importance index, time window index, and total path index. The distribution target weight is set to find the best target path, so as to realize the optimization of regional logistics distribution path. The experimental results show that the time for the designed optimization algorithm to find the optimal path is less than 2 minutes, while the time for the traditional algorithm to find the optimal path is more than 3 minutes, up to 4.6 minutes. The comparison shows that the traditional algorithm takes more time to find the optimal path in five times than the algorithm designed this time. Therefore, the optimization method of this design takes less time to find the optimal path than the traditional method, which proves the effectiveness of the regional logistics distribution path optimization algorithm.
Ingratiating with bosses for favourable performance ratings: a serial mediation mechanism

PurposeThis study examines how team-level mediating mechanisms (i.e. team psychological safety and team helping behaviour) facilitate the relationship between employees' ingratiation and performance ratings.Design/methodology/approachThe data were collected from 180 customer service teams working in Pakistan's hospitality industry through multiple sources using a paper and pencil questionnaire.FindingsThe multi-level structural equation modelling results showed that team psychological safety and helping behaviour fully mediated the relationship between ingratiation and performance ratings.Practical implicationsThe study has offered some implications for theory and practices.Originality/valueThis study showed that ingratiation might be a helpful impression management tool within a team context. Such an influence technique nurtures a psychologically safe climate and encourages peers to help each other perform mutual tasks within the hospitality context.
Detecting Appliances in Energy Traces

The amount of data gathered in the world is increasing every day. More and more energy data is being gathered from households and smart energy meters. To extend the functionality of these energy traces, Non-Intrusive Load Monitoring (NILM) algorithms can be used. These algorithms use training data in the form of appliancespecific energy trace to label different sections of the aggregated energy trace with activity. In this thesis, we investigate how to create a data set with the goal of using it to investigate NILM algorithms, and to build a platform for future student projects in the area of energy trace data sets. This platform contains suggestions on what methods to use for gathering data, how to store it, and how to analyse it.
Biophotonics in Dentistry

The aim of this review paper is to concentrate on the use and application of photonics in dentistry. More than one hundred review and research articles were comprehensively analysed in terms of applications of photonics in dentistry, including surgical applications, as well as dental biomaterials, diagnosis and treatments. In biomedical engineering, various fields, such as biology, chemistry, material and physics, come together in to tackle a disease/disorder either as a diagnostic tool or an option for treatment. Engineers believe that biophotonics is the application of photonics in medicine, whereas photonics is simply a technology for creating and connecting packets of light energy, known as photons. This review paper provides a comprehensive discussion of its main elements, such as photoelasticity, interferometry techniques, optical coherence tomography, different types of lasers, carbon nanotubes, graphene and quantum dots.
An assessment of serological techniques for the identification of asymptomatic visceral leishmaniasis in blood donors in Northeastern Brazil

Background: Visceral leishmaniasis (VL) is a neglected disease caused by species of the Leishmania donovani complex and transmitted mainly by the bite of infected sandflies. A less frequent form of transmission is the transfusion route, important in countries lacking screening procedures for VL in blood banks. Methods: To identify asymptomatic infection by Leishmania sp, our goal here was to evaluate and compare the performance of three different serological techniques: Flow Cytometry (FC); Direct Agglutination Test (DAT); and Immunoenzymatic Test (ELISA). Thirty-one asymptomatic carriers of L. infantum were selected, from a single blood bank located in Recife, northeastern Brazil. Results : Foi avaliada a frequência de amostras positivas assintomáticas nos testes, no ELISA baseado no antígeno solúvel de Leishmania (25,8%), FC (22,8%), DAT (9,7%) e ELISA baseado em uma única proteína recombinante (12,9%). A sensibilidade dos testes também foi avaliada, o FC (22,58%) e o ELISA com o antígeno solúvel (22,58%) apresentaram os maiores valores de sensibilidade que o DAT (6,45%) e o ELISA recombinante (12,9%). Apenas a CF apresentou baixa especificidade (22,22%), em contraste com as demais técnicas (100% de especificidade). Conclusion: Given the results presented, we highlight the low effectiveness of serological techniques for the identification of asymptomatic individuals, thus emphasizing the need to develop more accurate methods for this purpose.
Experimental Investigation of Thermal Performance of Solar Air Heater

The heat from the sun, coupled with the wind, has been used to dry and preserve food crops for several thousands of year instead of such a kind of drying occurs in the building terrace or in the open space, dryers are the most effective and efficient option for drying. All dryers need ventilation to able to dry crops effectively. Air movement can be by natural convection or by forced convection. By using modern solar dryer, better quality product can be obtain in less time and in more hygienic way. Double flow solar air heater is one of the effort in the direction of improving the efficiency of solar dryer with low cost.
Deciphering the Molecular Mechanisms of Chilling Tolerance in Lsi1-Overexpressing Rice

Improving tolerance to low-temperature stress during the rice seedling stage is of great significance in agricultural science. In this study, using the low silicon gene 1 (Lsi1)-overexpressing (Dular-OE) and wild-type rice (Dular-WT), we showed that Lsi1 overexpression enhances chilling tolerance in Dular-OE. The overexpression of the Lsi1 increases silicon absorption, but it was not the main reason for chilling tolerance in Dular-OE. Instead, our data suggest that the overexpression of a Lsi1-encoding NIP and its interaction with key proteins lead to chilling tolerance in Dular-OE. Additionally, we show that the high-mobility group protein (HMG1) binds to the promoter of Lsi1, positively regulating its expression. Moreover, Nod26-like major intrinsic protein (NIP)’s interaction with α and β subunits of ATP synthase and the 14-3-3f protein was validated by co-immunoprecipitation (Co-IP), bimolecular fluorescent complementary (BiFC), and GST-pulldown assays. Western blotting revealed that the overexpression of NIP positively regulates the ATP-synthase subunits that subsequently upregulate calcineurin B-like interacting protein kinases (CIPK) negatively regulating 14-3-3f. Overall, these NIP-mediated changes trigger corresponding pathways in an orderly manner, enhancing chilling tolerance in Dular-OE.
Self-Assembled PSMA-Targeted Nanoparticles Enhanced Photodynamic Therapy in Prostate Cancer

Prostate cancer greatly threatens human health. Many treatment strategies including surgery and radiotherapy are applied to treat prostate cancer. However, these treatment strategies will bring great side effects. Herein, we developed a functionalized Prostate-Specific Membrane Antigen-Chlorin e6 (PSMA-Ce6) NPs to enhance photodynamic therapy to reduce the side effects of prostate cancer treatments. We linked the hydrophobic photosensitizer Ce6 with the small hydrophilic molecule PSMA ligand through a covalent bond to form a functionalized PSMA-Ce6. In aqueous phase, the PSMA-Ce6 self-assembled into nanoparticles with a hydrophobic core and a hydrophilic shell, which significantly enhanced the accumulation of PSMA-Ce6 in the tumor. The molecularly targeted drug PSMA inhibitor can not only specifically inhibit PSMA but also increase the accumulation of the photosensitizer Ce6 in the tumor and achieve a completed tumor ablation under the near infrared (NIR) irradiation. PSMA-Ce6 NPs have great potential for translation into clinical applications.
Path Planning Method of Mobile Robot Using Improved Deep Reinforcement Learning

A mobile robot path planning method based on improved deep reinforcement learning is proposed. First, in order to conform to the actual kinematics model of the robot, the continuous environmental state space and discrete action state space are designed. In addition, an improved deep Q-network (DQN) method is proposed, which takes the directly collected information as the training samples and combines the environmental state characteristics of the robot and the target point to be reached as the input of the network. DQN method takes the Q value at the current position as the output of the network model and uses ε -greedy strategy for action selection. Finally, the reward function combined with the artificial potential field method is designed to optimize the state-action space. The reward function solves the problem of sparse reward in the environmental state space and makes the action selection of the robot more accurate. Experiments show that compared with the classical DQN method, the average loss function value is reduced by 36.87% and the average reward value is increased by 12.96%, which can effectively improve the working efficiency of mobile robot.
The Stibium Bond or the Antimony-Centered Pnictogen Bond: The Covalently Bound Antimony Atom in Molecular Entities in Crystal Lattices as a Pnictogen Bond Donor

A stibium bond, i.e., a non-covalent interaction formed by covalently or coordinately bound antimony, occurs in chemical systems when there is evidence of a net attractive interaction between the electrophilic region associated with an antimony atom and a nucleophile in another, or the same molecular entity. This is a pnictogen bond and are likely formed by the elements of the pnictogen family, Group 15, of the periodic table, and is an inter- or intra-molecular non-covalent interaction. This overview describes a set of illustrative crystal systems that were stabilized (at least partially) by means of stibium bonds, together with other non-covalent interactions (such as hydrogen bonds and halogen bonds), retrieved from either the Cambridge Structure Database (CSD) or the Inorganic Crystal Structure Database (ICSD). We demonstrate that these databases contain hundreds of crystal structures of various dimensions in which covalently or coordinately bound antimony atoms in molecular entities feature positive sites that productively interact with various Lewis bases containing O, N, F, Cl, Br, and I atoms in the same or different molecular entities, leading to the formation of stibium bonds, and hence, being partially responsible for the stability of the crystals. The geometric features, pro-molecular charge density isosurface topologies, and extrema of the molecular electrostatic potential model were collectively examined in some instances to illustrate the presence of Sb-centered pnictogen bonding in the representative crystal systems considered.
Design process of prestressed membrane formworks for thin-shell structures

This paper describes the digital design process of a fabric formwork applied to one of several adjacent concrete shell roofs for a private arts building to be constructed in 2017 in Brooklyn, NY, USA. This work is part of a larger feasibility study that compares this construction method to existing alternatives. Although the force distribution under load of the fresh concrete can be exactly computed for a fabric formwork, built examples show only modest spans and, where known, high construction tolerances. Among several causes, the main reason is thought to be the difficulty in reproducing the exact force distribution on site, which is more easily achieved in a discrete cable net than a continuous membrane formwork. In the latter case, the stress distribution is governed by the cutting patterns, making their design crucial to control of these tolerances. Towards this end, the design process laid out in this paper adapts the procedure for cable-net and fabric formworks to one for purely fabric formworks by computing stress distributions and flat cutting patterns.
Finite-Time Neural Network Fault-Tolerant Control for Robotic Manipulators under Multiple Constraints

In this study, a backstepping-based fault-tolerant controller for a robotic manipulator system with input and output constraints was developed. First, a barrier Lyapunov function was adopted to ensure that the system output satisfied time-varying constraints. Subsequently, the actuator input saturation and asymmetric dead-zone characteristics were also considered, and the actuator characteristics were described using a continuous function. The impacts of actuator failures and unknown dynamical parameters of the system were eliminated by employing Gaussian radial basis function neural networks. The external disturbances were compensated for, using a disturbance observer. Meanwhile, a finite-time dynamic surface technique was adopted to accelerate the convergence of the system errors. Finally, simulation of a 2-degrees-of-freedom robotic manipulator system showed the effectiveness of the proposed controller.
A newly reconciled dataset for identifying sea level rise and variability in Dublin Bay

Abstract. We provide an updated sea level dataset for Dublin for the period 1938–2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the mean low water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of sea level rise of 1.1 mm yr−1 during 1953–2016 (95 % credible interval from 0.6 to 1.6 mm yr−1), and a rate of 7 mm yr−1 during 1997–2016 (95 % credible interval from 5 to 8.8 mm yr−1). The overall sea level rise is in line with expected trends, but large multidecadal variability has led to higher rates of rise in recent years.
Food for Memory: A Review of the Effects of Diet on Alzheimer's Disease Progression

The incidence of Alzheimer disease (AD) is rapidly increasing worldwide. Alzheimer’s disease is a type of dementia characterized by progressive cognitive dysfunction. Though some studies suggest potential management options for preventing AD symptom progression, there is currently no absolute way to fully prevent Alzheimer’s disease from occuring nor is there a curative treatment. Despite the few methods associated with preventing AD symptom progression, evidence strongly suggests that diet may play a role in slowing AD symptom progression and decreasing disease burden. In this review, we evaluated several diets to understand their respective association with AD symptoms and neuropathology. Overall, we found that the Mediterranean, Vegan, Vegetarian, Ketogenic and Paleo diets improve AD cognitive symptoms and decrease amyloid β peptide (Aβ), the principal toxic factor in AD. However, further research needs to be conducted to fully elucidate the relationship between these diets and AD.
MicroRNA-26b Attenuates Platelet Adhesion and Aggregation in Mice

Platelets are key regulators of haemostasis, making platelet dysfunction a major driver of thrombosis. Numerous processes that determine platelet function are influenced by microRNAs (miRs). MiR-26b is one of the highest-expressed miRs in healthy platelets, and its expression in platelets is changed in a diseased state. However, the exact effect of this miR on platelet function has not been studied yet. In this study, we made use of a whole-body knockout of miR-26b in ApoE-deficient mice in order to determine its impact on platelet function, thrombus formation and platelet signalling both ex vivo and in vivo. We show that a whole-body deficiency of miR-26b exacerbated platelet adhesion and aggregation ex vivo. Additionally, in vivo, platelets adhered faster, and larger thrombi were formed in mice lacking miR-26b. Moreover, isolated platelets from miR-26b-deficient mice showed a hyperactivated Src and EGFR signalling. Taken together, we show here for the first time that miR-26b attenuates platelet adhesion and aggregation, possibly through Src and EGFR signalling.