Miracle-dz/UniFAR-FTUs huggingface dataset:
The contribution of preclinical magnetic resonance imaging and spectroscopy to Huntington’s disease

Huntington’s disease is an inherited disorder characterized by psychiatric, cognitive, and motor symptoms due to degeneration of medium spiny neurons in the striatum. A prodromal phase precedes the onset, lasting decades. Current biomarkers include clinical score and striatal atrophy using Magnetic Resonance Imaging (MRI). These markers lack sensitivity for subtle cellular changes during the prodromal phase. MRI and MR spectroscopy offer different contrasts for assessing metabolic, microstructural, functional, or vascular alterations in the disease. They have been used in patients and mouse models. Mouse models can be of great interest to study a specific mechanism of the degenerative process, allow better understanding of the pathogenesis from the prodromal to the symptomatic phase, and to evaluate therapeutic efficacy. Mouse models can be divided into three different constructions: transgenic mice expressing exon-1 of human huntingtin (HTT), mice with an artificial chromosome expressing full-length human HTT, and knock-in mouse models with CAG expansion inserted in the murine htt gene. Several studies have used MRI/S to characterized these models. However, the multiplicity of modalities and mouse models available complicates the understanding of this rich corpus. The present review aims at giving an overview of results obtained using MRI/S for each mouse model of HD, to provide a useful resource for the conception of neuroimaging studies using mouse models of HD. Finally, despite difficulties in translating preclinical protocols to clinical applications, many biomarkers identified in preclinical models have already been evaluated in patients. This review also aims to cover this aspect to demonstrate the importance of MRI/S for studying HD.
Exploring the secretome of Corynebacterium glutamicum ATCC 13032

The demand for alternative sources of food proteins is increasing due to the limitations and challenges associated with conventional food production. Advances in biotechnology have enabled the production of proteins using microorganisms, thus prompting the exploration of attractive microbial hosts capable of producing functional proteins in high titers. Corynebacterium glutamicum is widely used in industry for the production of amino acids and has many advantages as a host organism for recombinant protein production. However, its performance in this area is limited by low yields of target proteins and high levels of native protein secretion. Despite representing a challenge for heterologous protein production, the C. glutamicum secretome has not been fully characterized. In this study, state-of-the-art mass spectrometry-based proteomics was used to identify and analyze the proteins secreted by C. glutamicum. Both the wild-type strain and a strain that produced and secreted a recombinant β-lactoglobulin protein were analyzed. A total of 427 proteins were identified in the culture supernatants, with 148 predicted to possess a secretion signal peptide. MS-based proteomics on the secretome enabled a comprehensive characterization and quantification (based on abundance) of the secreted proteins through label-free quantification (LFQ). The top 12 most abundant proteins accounted for almost 80% of the secretome. These are uncharacterized proteins of unknown function, resuscitation promoting factors, protein PS1, Porin B, ABC-type transporter protein and hypothetical membrane protein. The data can be leveraged for protein production by, e.g., utilizing the signal peptides of the most abundant proteins to improve secretion of heterologous proteins. In addition, secretory stress can potentially be alleviated by inactivating non-essential secreted proteins. Here we provide targets by identifying the most abundant, secreted proteins of which majority are of unknown function. The data from this study can thus provide valuable insight for researchers looking to improve protein secretion and optimize C. glutamicum as a host for secretory protein production.
Remote Evaluation of Sleep and Circadian Rhythms in Older Adults With Mild Cognitive Impairment and Dementia: Protocol for a Feasibility and Acceptability Mixed Methods Study.

BACKGROUND Sleep disturbances are a potentially modifiable risk factor for neurodegenerative dementia secondary to Alzheimer disease (AD) and Lewy body disease (LBD). Therefore, we need to identify the best methods to study sleep in this population. OBJECTIVE This study will assess the feasibility and acceptability of various wearable devices, smart devices, and remote study tasks in sleep and cognition research for people with AD and LBD. METHODS We will deliver a feasibility and acceptability study alongside a prospective observational cohort study assessing sleep and cognition longitudinally in the home environment. Adults aged older than 50 years who were diagnosed with mild to moderate dementia or mild cognitive impairment (MCI) due to probable AD or LBD and age-matched controls will be eligible. Exclusion criteria include lack of capacity to consent to research, other causes of MCI or dementia, and clinically significant sleep disorders. Participants will complete a cognitive assessment and questionnaires with a researcher and receive training and instructions for at-home study tasks across 8 weeks. At-home study tasks include remote sleep assessments using wearable devices (electroencephalography headband and actigraphy watch), app-based sleep diaries, online cognitive assessments, and saliva samples for melatonin- and cortisol-derived circadian markers. Feasibility outcomes will be assessed relating to recruitment and retention, data completeness, data quality, and support required. Feedback on acceptability and usability will be collected throughout the study period and end-of-study interviews will be analyzed using thematic analysis. RESULTS Recruitment started in February 2022. Data collection is ongoing, with final data expected in February 2024 and data analysis and publication of findings scheduled for the summer of 2024. CONCLUSIONS This study will allow us to assess if remote testing using smart devices and wearable technology is a viable alternative to traditional sleep measurements, such as polysomnography and questionnaires, in older adults with and without MCI or dementia due to AD or LBD. Understanding participant experience and the barriers and facilitators to technology use for research purposes and remote research in this population will assist with the development of, recruitment to, and retention within future research projects studying sleep and cognition outside of the clinic or laboratory. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/52652.
Propofol anesthesia improves stroke outcomes over isoflurane anesthesia—a longitudinal multiparametric MRI study in a rodent model of transient middle cerebral artery occlusion

General anesthesia is routinely used in endovascular thrombectomy procedures, for which volatile gas and/or intravenous propofol are recommended. Emerging evidence suggests propofol may have superior effects on disability and/or mortality rates, but a mode-of-action underlying these class-specific effects remains unknown. Here, a moderate isoflurane or propofol dosage on experimental stroke outcomes was retrospectively compared using serial multiparametric MRI and behavioral testing. Adult male rats (N = 26) were subjected to 90-min filament-induced transient middle cerebral artery occlusion. Diffusion-, T2- and perfusion-weighted MRI was performed during occlusion, 0.5 h after recanalization, and four days into the subacute phase. Sequels of ischemic damage—blood–brain barrier integrity, cerebrovascular reactivity and sensorimotor functioning—were assessed after four days. While size and severity of ischemia was comparable between groups during occlusion, isoflurane anesthesia was associated with larger lesion sizes and worsened sensorimotor functioning at follow-up. MRI markers indicated that cytotoxic edema persisted locally in the isoflurane group early after recanalization, coinciding with burgeoning vasogenic edema. At follow-up, sequels of ischemia were further aggravated in the post-ischemic lesion, manifesting as increased blood–brain barrier leakage, cerebrovascular paralysis and cerebral hyperperfusion. These findings shed new light on how isoflurane, and possibly similar volatile agents, associate with persisting injurious processes after recanalization that contribute to suboptimal treatment outcome.
A Study on the Relationship between Corporate Social Responsibility Strategic Process and Operational Performance of Multinational Manufacturing Enterprises under the Perspective of Environmental Management

Under the effects of economic globalization and environmental protection & social problems in the post-crisis era, the corporate social responsibility (CSR) strategy and activities are gradually emphasized in multinational manufacturing enterprises. Accordingly, the theoretical relationship among CSR strategy, CSR activities, and operational performance is proposed in this study, and the moderation effect of external environmental management on the relationship is further discussed. Based on the survey data of 598 manufacturing enterprises from 22 countries and regions for 2013 International Manufacturing Strategy Survey (IMSS VI), the empirical research results reveal (1)significantly positive effects of CSR strategy on internal CSR activities and external CSR activities of multinational manufacturing enterprises, (2)remarkably positive effects of internal CSR activities and external CSR activities on efficient performance, flexible performance, and cost performance in the operational performance of multinational manufacturing enterprises, and (3)partially notable moderation of external environmental management on the relationship between CSR activities and operational performance of multinational manufacturing enterprises. On one hand, this study expands the theoretical research on corporate social responsibility; on the other hand, it presents certain practice value of the formulation and practice of CSR strategy in multinational manufacturing enterprises.
The Hungarian Longitudinal Growth Study: From birth to the age of 18 years

This Working Paper presents the main results of the ‘Hungarian Longitudinal Growth Study’. It was the first longitudinal, nationally representative research programme in Hungary that focused on children aged between birth and 18 years of age; the number of subjects was 2,984 boys and 2,701 girls at birth, and 516 boys and 523 girls at 18 years of age. The volume is divided in four sections. The first introduces the sampling procedure and the confidence parameters of the studied anthropometric dimensions. The second represents the age-related reference values (mean, standard deviations, centiles) and growth charts (centile distribution) of body length/height, body weight, body mass index, weight-for-height, head circumference, chest circumference, abdominal circumference and skinfold measurements (triceps, subscapular, iliospinal, abdominal) between 0 and 18 years of age. The third section presents the first reference values and charts of body height velocity between three and 18 years of age. From the total sample 325 boys’ and 309 girls’ individual growth data (aged between three and 18 years) were used to construct these body height velocity references. The fourth section summarises the main results concerning menarcheal age in the studied representative sample. The mean age of menarche was 12.59 years (± 0.96 years SD, n: 1709 girls). By considering the mean age at peak height velocity (PHV: 8.32 cm/year) in girls, i.e. 11.2 years of age and this indicator of sexual maturation, it was possible to state that menarche occurred 16.44 months later than the mean age at PHV in girls in the sample. The differences between mature and non-mature girls (this division was made on the basis of menarche) in body height, body weight, body mass index and chest circumference, were also analysed in the last section. Printed publications arising from research results of the Hungarian Longitudinal Growth Study are listed at the end of the paper.
Energy Consumption Investigations in WSN using OMNeT

Wireless sensor networks have attracted a lot of interest by various industries for its ability to perform real time measurements in a cost effective way. Since the sensor nodes often rely on batteries to perform their task, a lot of research have been focused on minimizing the energy consumption in WSN. In this project TDMA based WSN was investigated to see if the energy consumption could be minimized by manipulating the nodes communication range. Though a lot of research has been focused on similar problems exist these are mostly focused on non TDMA network. The problem was partially analysed analytically, but the mainly assessed by simulation, using the OMNeT++ simulation framework. Since OMNeT++ is not a simulator by it self, the MiXiM model framework that focuses on the simulation of wireless networks was used. It was shown that a minimum point for energy consumption can be found. However, a small degradation in the quality of the links may cause a dramatic increase in the energy consumption due to retransmissions.
Clostridium strain FAM25158, a unique endospore-forming bacterium related to Clostridium tyrobutyricum and isolated from Emmental cheese shows low tolerance to salt

The genus Clostridium is a large and diverse group of species that can cause food spoilage, including late blowing defect (LBD) in cheese. In this study, we investigated the taxonomic status of strain FAM25158 isolated from Emmental cheese with LBD using a polyphasic taxonomic and comparative genomic approach. A 16S rRNA gene sequence phylogeny suggested affiliation to the Clostridium sensu stricto cluster, with Clostridium tyrobutyricum DSM 2637T being the closest related type strain (99.16% sequence similarity). Average Nucleotide Identity (ANI) analysis revealed that strain FAM25158 is at the species threshold with C. tyrobutyricum, with ANI values ranging from 94.70 to 95.26%, while the digital DNA-DNA hybridization values were below the recommended threshold, suggesting that FAM25158 is significantly different from C. tyrobutyricum at the genomic level. Moreover, comparative genomic analysis between FAM25158 and its four closest C. tyrobutyricum relatives revealed a diversity of metabolic pathways, with FAM25158 differing from other C. tyrobutyricum strains by the presence of genes such as scrA, srcB, and scrK, responsible for sucrose utilization, and the absence of many important functional genes associated with cold and osmolality adaptation, which was further supported by phenotypic analyses. Surprisingly, strain FAM25158 exhibited unique physiologic traits, such as an optimal growth temperature of 30°C, in contrast to its closest relatives, C. tyrobutyricum species with an optimal growth temperature of 37°C. Additionally, the growth of FAM25158 was inhibited at NaCl concentrations higher than 0.5%, a remarkable observation considering its origin from cheese. While the results of this study provide novel information on the genetic content of strain FAM25158, the relationship between its genetic content and the observed phenotype remains a topic requiring further investigation.
A Cloud Monitor to Reduce Energy Consumption With Constrained Optimization of Server Loads

As the energy consumption of cloud computing increases, resource optimization becomes more important in this dynamically changing computation environment. Cloud monitor is an effort for better job distribution models and cloud load optimization on cloud computing platforms to reduce energy consumption, decrease computation and waiting time in the queue, and improve the overall utilization of resources. We use linear programming to optimize the distribution of jobs whose arrival and duration times are Poisson distributed to the servers. Our simulations show 1) improvements in job distributions with better correlation with the energy consumption of servers, 2) possible increased utilization of resources in some settings, and 3) significant reductions in energy consumption that reach 42%. We present the mathematical formulation of our model as well as how to represent it as a linear programming problem. The constraints introduced allow the mathematical definition of the optimal distribution, optimal redistribution, and optimal elasticity for cloud computing. Cloud monitor has potential to contribute to more sustainable and efficient cloud computing systems.
Lung Parenchyma Segmentation Based on U-Net Fused With Shape Stream

Accurate lung parenchyma segmentation is vital for computer-aided lung cancer diagnosis. Existing lung parenchyma segmentation networks excel at segmenting large and clear lung parenchyma regions, but struggle with small and blurry regions. This study proposes an improved network structure to improve the segmentation performance of small and blurry lung parenchyma regions while maintaining accuracy for large and clear lung parenchyma regions. The proposed network is an improved network based on U-Net. A shape stream branch and multi-scale convolutional blocks are introduced into the network. The proposed network takes computed tomography (CT) images as inputs and generates corresponding binary masks as outputs. In this study, the original CT images are derived from the Open Source Imaging Consortium (OSIC) Pulmonary Fibrosis Progression dataset. In the experimental results, the overall mean Dice Similarity Coefficient (DSC) of the proposed network reaches 0.932907, which is 0.64% higher than that of the second-best network. The segment test results show that the proposed network has significantly better segmentation performance than other networks when the lung parenchyma is relatively small. On a patient-by-patient basis, the mean DSC of the proposed network is 0.63% higher than that of the second-best network. The proposed network improves the segmentation performance of small and blurry lung parenchyma while maintaining accuracy for large and clear lung parenchyma.
3D feature point extraction from LiDAR data using a neural network

Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles.
Experimental Study on Seepage-induced Deformation of Light Silt Sandy Loam in Dongtai, Jiangsu Province

Seepage-induced deformation experiments were conducted on the light silt sandy loam at Dongtai, Jiangsu Province. The mechanism of seepage-induced deformation is studied based on the test results. It is ascertained that the type of seepage failure was soil flow according to the experiment results. Threshold and critical hydraulic gradient i t and i c can be figured out from the i -log v curve. The i c value from the experiment is smaller than that from the Terzaghi formula. A modifier formula was proposed on the basis of analysis of the experimental results. The experiment results show a linear relationship between D r and i t , while the relationship between D r and i c meets a quadratic equation.
Nursing Instructors’ and Students’ Experiences of Transition from Face-to-Face Education to Virtual Education during the Coronavirus Disease 2019 Pandemic

Introduction. The coronavirus disease 2019 (COVID-19) pandemic necessitated the suspension of most educational activities and a shift from face-to-face (FTF) education to virtual education (VE). These changes were associated with new educational experiences for both instructors and students. This study aimed to explore nursing instructors’ and students’ experiences of VE during the COVID-19 pandemic. Method. This descriptive qualitative study was carried out in 2021. Participants were 21 bachelor’s, master’s, and Ph.D. nursing students and six nursing instructors purposively selected from the Faculty of Nursing and Midwifery of Tehran University of Medical Sciences, Tehran, Iran. Data were gathered through FTF or online in-depth semistructured interviews and were analyzed through conventional qualitative content analysis as proposed by Graneheim and Lundman (2004). Results. Eight subthemes and three main themes were developed during data analysis. The main themes and subthemes were VE as a double-edged sword (VE as a threat, VE as an opportunity, and necessity to improve VE), weaknesses in clinical education (reduced quality of clinical education and necessity to compensate for students’ retarded clinical learning), and reduced reliability of virtual student evaluation (cheating as a problem of virtual evaluation, stresses and tensions of virtual evaluation, and necessity to use new strategies in virtual evaluation). Conclusion. Despite the different shortcomings and challenges of VE during the COVID-19 pandemic, educational experiences gained in this period can be used to improve the quality of nursing VE in future epidemics and pandemics.
Android's External Device Attack: Demonstration and Security Suggestions

A large number of users choose to manage their Android phones via assistant software based on the ADB tool. These users face threats of a variety of attacks, such as external device attacks, because the current Android system’s security mechanism does not pay sufficient attention to the safety of the ADB tool. This paper provides a detailed introduction of the ADB tool and discusses threats its use can bring. These threats include silently installing malicious apps, pushing malicious privilege escalation files to the phone, copying private files from the phone, risking opening TCP debugging mode, and introducing vulnerabilities by the ADB tool. We demonstrate a peripheral device attack is presented to help understand how the designed and implemented flaws can be used to bypass security protection. The demonstration is realised by Raspberry Pi, an open-source single chip computer that can run an ARM-based Linux system. Finally, some suggestions are proposed for security improvements to address threats introduced by the ADB tool.