Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuronal activity induces glucosylceramide that is secreted via exosomes for lysosomal degradation in glia

Recessive variants in GBA1 cause Gaucher disease, a prevalent form of lysosome storage disease. GBA1 encodes a lysosomal enzyme that hydrolyzes glucosylceramide (GlcCer) into glucose and ceramide. Its loss causes lysosomal dysfunction and increased levels of GlcCer. We generated a null allele of the Drosophila ortholog Gba1b by inserting the Gal4 using CRISPR-Cas9. Here, we show that Gba1b is expressed in glia but not in neurons. Glial-specific knockdown recapitulates the defects found in Gba1b mutants, and these can be rescued by glial expression of human GBA1. We show that GlcCer is synthesized upon neuronal activity, and it is transported from neurons to glia through exosomes. Furthermore, we found that glial TGF-β/BMP induces the transfer of GlcCer from neurons to glia and that the White protein, an ABCG transporter, promotes GlcCer trafficking to glial lysosomes for degradation.
Feshbach resonances in ultracold 85Rb-87Rb and 6Li-87Rb mixtures

We present an analysis of experimentally accessible magnetic Feshbach resonances in ultra-cold hetero-nuclear 85Rb-87Rb and 6Li-87Rb mixtures. Using recent experimental measurements of the triplet scattering lengths for 6Li-87Rb and 7Li-87Rb mixtures and Feshbach resonances for one combination of atomic states, we create model potential curves and fine tune them to reproduce the measured resonances and to predict the location of several experimentally relevant resonances in Li-Rb collisions. To model 85Rb-87Rb collisions, we use accurate Rb_2 potentials obtained previously from the analysis of experiments on 87Rb-87Rb collisions. We find resonances that occur at very low magnetic fields, below 10 G, which may be useful for entanglement generation in optical lattices or atom chip magnetic traps.
A New Ensemble Approach for Congestive Heart Failure and Arrhythmia Classification Using Shifted One-Dimensional Local Binary Patterns with Long Short-Term Memory

The electrocardiogram (ECG) is a vital diagnostic tool for identifying a variety of cardiac disorders, including cardiac arrhythmia (ARR), sinus rhythms and heart failure. However, rapid interpretation of ECG recordings is quite important in the diagnosis of heart-related diseases. Many patients can be saved using the systems developed for the rapid and accurate analysis of ECG signals. A novel ensemble method based on shifted one-dimensional local binary patterns (S-1D-LBP) and long short-term memory (LSTM) is presented for the prognosis of ARR, normal sinus rhythm (NSR) and congestive heart failure (CHF) in this study. The ECG signals were first subjected to the S-1D-LBP method. Depending on the R and L parameters of this method, nine different signals are generated. Each of the histograms of these signals is given to LSTM models with the same hyperparameters. ECG signals are classified according to the common decisions of LSTM models with nine different input signals. The suggested method was tested using ECG signals (ARR, NSR and CHF) from the MIT-BIH and BIDMC datasets. Considering the results obtained in the applications carried out with various scenarios, it was observed that a high (99.6%) success rate was attained by the proposed approach. The suggested S-1D-LBP + ELSTM (Ensemble LSTM) model is expected to be safe to employ in the classification of various signals.
Biomaterial-Mediated Factor Delivery for Spinal Cord Injury Treatment

Spinal cord injury (SCI) is an injurious process that begins with immediate physical damage to the spinal cord and associated tissues during an acute traumatic event. However, the tissue damage expands in both intensity and volume in the subsequent subacute phase. At this stage, numerous events exacerbate the pathological condition, and therein lies the main cause of post-traumatic neural degeneration, which then ends with the chronic phase. In recent years, therapeutic interventions addressing different neurodegenerative mechanisms have been proposed, but have met with limited success when translated into clinical settings. The underlying reasons for this are that the pathogenesis of SCI is a continued multifactorial disease, and the treatment of only one factor is not sufficient to curb neural degeneration and resulting paralysis. Recent advances have led to the development of biomaterials aiming to promote in situ combinatorial strategies using drugs/biomolecules to achieve a maximized multitarget approach. This review provides an overview of single and combinatorial regenerative-factor-based treatments as well as potential delivery options to treat SCIs.
Regional economic resilience: towards a system approach

ABSTRACT This paper proposes a system approach to regional economic resilience. This approach argues that regional economies undergo, to varying degrees, changes to their economic system that result from the collective but uncoordinated action of economic actors in an attempt to be resilient to shocks. The system change, particularly focusing on changes to economies’ structure and function, which occurs during and following a shock, determines the type of resilience (i.e., engineering, ecological, evolutionary and transformative) employed by regions. The type of resilience employed can influence regions’ long-term growth trajectory and resilience to future shocks. This approach advances the examination of regions’ resilience capacity, which has largely been ignored in empirical studies of resilience. In doing so, the approach developed in this paper is heuristic rather than deterministic, with the latter characterizing the bulk of the literature. A greater investigation into system change can provide a holistic understanding of resilience. This approach has many advantages, such as developing greater insight into resilience, applying a heuristic method rather than deterministic and examining regions’ adaptive capacity. To advance the system approach, this paper provides greater conceptual clarity of resilience, highlighting the notions conceptual parameters and rethinking the oppositional context in which the four main types of resilience are commonly discussed. Specifically, it conceptualizes the main types of resilience as complementary rather than oppositional. The overall contribution of this paper is twofold. First, it establishes a greater conceptual framework of resilience. Second, it develops an approach in which regions’ adaptive capacity can be investigated.
Quantification of mutant–allele expression at isoform level in cancer from RNA-seq data

Abstract Even though the role of DNA mutations in cancer is well recognized, current quantification of the RNA expression, performed either at gene or isoform level, typically ignores the mutation status. Standard methods for estimating allele-specific expression (ASE) consider gene-level expression, but the functional impact of a mutation is best assessed at isoform level. Hence our goal is to quantify the mutant–allele expression at isoform level. We have developed and implemented a method, named MAX, for quantifying mutant–allele expression given a list of mutations. For a gene of interest, a mutant reference is constructed by incorporating all possible mutant versions of the wild-type isoforms in the transcriptome annotation. The mutant reference is then used for the RNA-seq reads mapping, which in principle works similarly for any quantification tool. We apply an alternating EM algorithm to the read-count data from the mapping step. In a simulation study, MAX performs well against standard isoform-quantification methods. Also, MAX achieves higher accuracy than conventional gene-based ASE methods such as ASEP. An analysis of a real dataset of acute myeloid leukemia reveals a subgroup of NPM1-mutated patients responding well to a kinase inhibitor. Our findings indicate that quantification of mutant–allele expression at isoform level is feasible and has potential added values for assessing the functional impact of DNA mutations in cancers.
An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet

Due to the poor adaptability of traditional methods in the cigarette detection task on the automatic cigarette production line, it is difficult to accurately identify whether a cigarette has defects and the types of defects; thus, a cigarette appearance defect detection method based on C-CenterNet is proposed. This detector uses keypoint estimation to locate center points and regresses all other defect properties. Firstly, Resnet50 is used as the backbone feature extraction network, and the convolutional block attention mechanism (CBAM) is introduced to enhance the network’s ability to extract effective features and reduce the interference of non-target information. At the same time, the feature pyramid network is used to enhance the feature extraction of each layer. Then, deformable convolution is used to replace part of the common convolution to enhance the learning ability of different shape defects. Finally, the activation function ACON (ActivateOrNot) is used instead of the ReLU activation function, and the activation operation of some neurons is adaptively selected to improve the detection accuracy of the network. The experimental results are mainly acquired via the mean Average Precision (mAP). The experimental results show that the mAP of the C-CenterNet model applied in the cigarette appearance defect detection task is 95.01%. Compared with the original CenterNet model, the model’s success rate is increased by 6.14%, so it can meet the requirements of precision and adaptability in cigarette detection tasks on the automatic cigarette production line.
NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains

In the recent years, Distributed Denial of Service (DDoS) attacks on Internet of Things (IoT) devices have become one of the prime concerns to Internet users around the world. One of the sources of the attacks on IoT ecosystems are botnets. Intruders force IoT devices to become unavailable for its legitimate users by sending large number of messages within a short interval. This study proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN) based Distributed Denial of Services (DDoS) attack detection framework with mutual correlation as feature selection method which produces a superior result when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the detection performance achieves approximately 99% accuracy for the detection of attacks from botnets. In this work, we have designed and compared four different models where two are based on ANN and the other two are based on LSTM to detect the attack types of DDoS.
Association of High-Density Lipoprotein Cholesterol, Renal Function, and Metabolic Syndrome: An Assessment of the 2013–2018 National Health and Nutrition Examination Surveys

Previous findings assessing the relationship between high-density lipoprotein cholesterol (HDL-c) and kidney function have demonstrated contradictory results including positive, negative, and U-shaped relationships. Many prior studies in this area have been conducted in healthy populations, but few have considered the influence of metabolic health status. In the present study, a cross-sectional analysis was conducted using complex survey sample weighting in the assessment of 6455 subjects from the 2013–2018 National Health and Nutrition Examination Surveys (NHANES), representative of 94,993,502 United States citizens. Subjects were classified as metabolically healthy or unhealthy and linear regression analyses were performed to evaluate the influence of HDL-c on estimated glomerular filtration rate (eGFR). HDL-c was found to be negatively associated with eGFR in the metabolically healthy, unhealthy, and combined groups (B = −0.16, p < 0.0001, B = −0.21, p < 0.0001, and B = −0.05, p = 0.0211, respectively). This relationship persisted after adjustment for confounding variables (B = −0.24, p < 0.0001, B = −0.17, p < 0.001, and B = −0.18, p < 0.0001, respectively). The relationship between HDL-c and eGFR was found to be a negative linear association, rather than a U-shaped association, and it persisted in all models tested, despite statistical adjustment for confounding variables. After controlling the samples for outliers, the negative relationship between HDL-c and eGFR was attenuated in the healthy and total groups but remained significant in the MetS group, indicating a stronger relationship between HDL-c and eGFR in those with poorer health.
Recovery in environmental risk assessments at EFSA

EFSA performs environmental risk assessments (ERAs) for single potential stressors such as plant protection products, genetically modified organisms and feed additives and for invasive alien species that are harmful for plant health. In this risk assessment domain, the EFSA Scientific Committee recognises the importance of more integrated ERAs considering both the local and landscape scales, as well as the possible co-occurrence of multiple potential stressors that fall under the remit of EFSA, which are important when addressing ecological recovery. In this scientific opinion, the Scientific Committee gathered scientific knowledge on the potential for the recovery of non-target organisms for the further development of ERA. Current EFSA guidance documents and opinions were reviewed on how ecological recovery is addressed in ERA schemes. In addition, this scientific opinion is based on expert knowledge and data retrieved from the literature. Finally, the information presented in this opinion was reviewed by experts from the relevant EFSA Panels, European risk assessment bodies and through an open consultation requesting input from stakeholders. A conceptual framework was developed to address ecological recovery for any assessed products, and invasive alien species that are harmful for plant health. This framework proposes an integrative approach based on well-defined specific protection goals, scientific knowledge derived by means of experimentation, modelling and monitoring, and the selection of focal taxa, communities, processes and landscapes to develop environmental scenarios to allow the assessment of recovery of organisms and ecological processes at relevant spatial and temporal scales.
Dietary tea tree (Melaleuca alternifolia) oil supplementation enhances the expressions of amino acid transporters in goat ileal mucosa and improves intestinal immunity

Abstract Tea tree oil (TTO) is a plant‐derived additive with anti‐inflammatory, bactericidal, and growth‐promoting properties. However, little is known about the effects of TTO on intestinal amino acid transport and immune function in goats. Twenty‐four Ganxi goats (initial body weight of 13.5 ± 0.70 kg) were randomly allotted two treatments and fed either control (CON) or CON+TTO (0.2 ml/kg) diet. The addition of TTO to the diet significantly decreased (p < .05) tumor necrosis factor‐α content and increased (p < .05) interleukin‐2 (IL‐2) content in goat serum; significantly decreased (p < .05) IL‐12, and increased (p < .05) IL‐2 content in goat ileal mucosa; significantly increased (p < .05) secreted IgA content in the jejunal and ileal mucosa; significantly upregulated (p < .05) IL‐2 and downregulated (p < .05) IL‐12 at the mRNA level in the ileal mucosa; significantly elevated the levels of serine, arginine, and total amino acids in the ileal mucosa (p < .05); significantly upregulated (p < .05) SLC1A1 and SLC7A1 in the ileum; and significantly enhanced (p < .05) the protein expression of Claudin‐1 in the ileal mucosa. In summary, adding 0.2 ml/kg of TTO to the diet enhanced SLC1A1 and SLC7A1 mRNA expression in the ileal mucosa, and SLC1A1 and SLC7A1 could transport serine and arginine from the chyme to the ileal mucosa. Thus, increased serine and arginine content in the mucosa could improve intestinal immunity. TTO supplementation upregulated the expression of IL‐2 and Claudin‐1 in goat ileal mucosa, and enhanced immune function in the intestine.
Droughts and Mega-Droughts

ABSTRACT Drought is the deadliest natural disaster on Earth due to its long duration, wide spatial coverage and direct connection with the food supply for human beings. Drought develops slowly and thus is called a silent killer. This paper reviews the history of research on droughts and mega-droughts. Interannual droughts are in many places driven by the El Nino-Southern Oscillation, multi-decadal mega-droughts are often driven by the Atlantic Multi-Decadal Oscillation, while centennial mega-droughts are often driven by the Global Inter-Centennial Oscillation. More generally, droughts are affected by multiple factors including global sea surface temperature anomalies, local land-atmosphere feedbacks, internal atmospheric variability and external forcings from outside the climate system. Possible future research directions are also suggested.
Glioblastoma Multiforme Selective Nanomedicines for Improved Anti-Cancer Treatments

Glioblastoma Multiforme (GBM) is a devastating disease with a low survival rate and few efficacious treatment options. The fast growth, late diagnostics, and off-target toxicity of currently used drugs represent major barriers that need to be overcome to provide a viable cure. Nanomedicines (NMeds) offer a way to overcome these pitfalls by protecting and loading drugs, increasing blood half-life, and being targetable with specific ligands on their surface. In this study, the FDA-approved polymer poly (lactic-co-glycolic) acid was used to optimise NMeds that were surface modified with a series of potential GBM-specific ligands. The NMeds were fully characterised for their physical and chemical properties, and then in vitro testing was performed to evaluate cell uptake and GBM cell specificity. While all targeted NMeds showed improved uptake, only those decorated with the-cell surface vimentin antibody M08 showed specificity for GBM over healthy cells. Finally, the most promising targeted NMed candidate was loaded with the well-known chemotherapeutic, paclitaxel, to confirm targeting and therapeutic effects in C6 GBM cells. These results demonstrate the importance of using well-optimised NMeds targeted with novel ligands to advance delivery and pharmaceutical effects against diseased cells while minimising the risk for nearby healthy cells.
Streamlining Culture Conditions for the Neuroblastoma Cell Line SH-SY5Y: A Prerequisite for Functional Studies

The neuroblastoma cell line SH-SY5Y has been a well-established and very popular in vitro model in neuroscience for decades, especially focusing on neurodevelopmental disorders, such as Parkinson’s disease. The ability of this cell type to differentiate compared with other models in neurobiology makes it one of the few suitable models without having to rely on a primary culture of neuronal cells. Over the years, various, partly contradictory, methods of cultivation have been reported. This study is intended to provide a comprehensive guide to the in vitro cultivation of undifferentiated SH-SY5Y cells. For this purpose, the morphology of the cell line and the differentiation of the individual subtypes are described, and instructions for cell culture practice and long-term cryoconservation are provided. We describe the key growth characteristics of this cell line, including proliferation and confluency data, optimal initial seeding cell numbers, and a comparison of different culture media and cell viability during cultivation. Furthermore, applying an optimized protocol in a long-term cultivation over 60 days, we show that cumulative population doubling (CPD) is constant over time and does not decrease with incremental passage, enabling stable cultivation, for example, for recurrent differentiation to achieve the highest possible reproducibility in subsequent analyses. Therefore, we provide a solid guidance for future research that employs the neuroblastoma cell line SH-SY5Y.
On the Maximum Energy Efficiency of Random Access-Based OMA and NOMA in Multirate Environment

The paradigm of the upcoming 5G and beyond is the machine type communications (MTC), where a large number of devices automatically operate wireless communications. Due to their automatic and energy-intensive operations, energy efficiency (EE) becomes very important, but there is a lack of investigation for EE of random access networks, which is the underlying platform for MTC. In this paper, we focus on the EE of random access orthogonal and non-orthogonal multiple access (OMA and NOMA) networks. We first construct mathematical models of their EE performances. Information-theoretic theories are employed to transform probabilistic decodability into deterministic form for OMA, and along with the unique features of NOMA decoding process, by pivoting around the decodability of a specific packet, we realize the modeling for NOMA. Then, based on the established models, exploiting the nature of random access networks, a complementary geometric programming approach is used for performance optimization. From our investigation, the following results are discovered: (1) NOMA has significant advantage over OMA in EE performance due to its strong data recovery; (2) to maximize the EE performance, devices with higher data rate should transmit more frequently, but with lower transmission power; (3) lower data rates have beneficial effects on EE.
Responses of compound daytime and nighttime warm-dry and warm-humid events to individual anthropogenic forcings

Daytime heat is often associated with reduced soil moisture and cloud cover, while nighttime heat is connected to high humidity and increased cloud cover. Due to these differing mechanisms, compound daytime and nighttime heat events may respond differently to major anthropogenic forcings (greenhouse gases, anthropogenic aerosols, land-use and land-cover change). Here, we use GISS ModelE2.1-G historical single-forcing runs from 1955 to 2014 to examine how individual anthropogenic forcings affect compound heat events—specifically warm daytime and nighttime temperatures compounded with dry precipitation or high humidity conditions. We show that greenhouse gases alone amplify the natural frequency of warm-dry events by 1.5–5 times and warm-humid events by 2–9 times in tropical and extratropical latitudes. Conversely, aerosols and land-use/land-cover change reduce the frequency of these events, resulting in more modest increases and in some regions, declines, in the historical ‘all-forcings’ scenario. Individually, aerosol effects are stronger and more widespread compared to land-use, oftentimes reducing the natural frequency of these events by 60%–100%. The responses of these compound events are primarily driven by changes in daytime and nighttime temperatures through large-scale warming via greenhouse gases and cooling from aerosols and land-use/land-cover change. However, changes in warm-dry events are amplified in regions with concurrent precipitation declines (e.g. Central America, Mediterranean regions) and warm-humid events are amplified by global concurrent humidity increases. Additionally, we find differences between daytime and nighttime compound responses in the historical experiment that can be traced back to the individual forcings. In particular, aerosols produce a greater cooling effect on daytime relative to nighttime temperatures, which notably results in a historical reduction of Northern Hemisphere daytime warm-dry events relative to natural conditions. Our analysis provides a more comprehensive understanding of the significant impacts of different anthropogenic climate forcings on daytime and nighttime warm-dry and warm-humid events, informing future risk and impact assessments.