Miracle-dz/UniFAR-FTUs huggingface dataset:
Nasopharyngeal Microbiomes in Donkeys Shedding Streptococcus equi Subspecies equi in Comparison to Healthy Donkeys

Streptococcus equi subsp. equi (S. equi) is the pathogen causing strangles, a highly infectious disease that can affect equids including donkeys of all ages. It can persistently colonize the upper respiratory tract of animals asymptomatically for years, which serves as a source of infection. Several strangles outbreaks have been reported in the donkey industry in China in the last few years and pose a great threat to health, production, and the welfare of donkeys. Nasopharyngeal swab samples for culture and PCR are used widely in strangles diagnosis. Additionally, microbiomes within and on the body are essential to host homoeostasis and health. Therefore, the microbiome of the equid nasopharynx may provide insights into the health of the upper respiratory tract in animals. There has been no study investigating the nasopharyngeal microbiome in healthy donkeys, nor in donkeys shedding S. equi. This study aimed to compare nasopharyngeal microbiomes in healthy and carrier donkeys using 16S rRNA gene sequencing. Nasopharyngeal samples were obtained from 16 donkeys recovered from strangles (group S) and 14 healthy donkeys with no history of strangles exposure (group H). Of those sampled, 7 donkeys were determined to be carriers with positive PCR and culture results in group S. In group H, all 14 donkeys were considered free of strangles based on the history of negative exposure, negative results of PCR and culture. Samples from these 21 donkeys were used for microbial analysis. The nasopharyngeal microbiome composition was compared between the two groups. At the phylum level, relative abundance of Proteobacteria was predominantly higher in the S. equi carrier donkeys than in healthy donkeys (P < 0.01), while Firmicutes and Actinobacteria were significantly less abundant in the S. equi carrier donkeys than in healthy donkeys (P < 0.05). At the genus level, Nicoletella was detected in the upper respiratory tract of donkeys for the first time and dominated in carrier donkeys. It is suspected to suppress other normal flora of URT microbiota including Streptococcus spp. , Staphylococcus spp. , and Corynebacterium spp. We concluded that the nasopharyngeal microbiome in S. equi carrier donkeys still exhibited microbial dysbiosis, which might predispose them to other airway diseases.
Impaired HSP70 Expression in the Aorta of Female Rats: A Novel Insight Into Sex-Specific Differences in Vascular Function

Heat-shock protein 70 (HSP70) contributes to cellular calcium (Ca2+) handling mechanisms during receptor-mediated vascular contraction. Interestingly, previous studies have independently reported sex-related differences in HSP70 expression and Ca2+ dynamics. Still, it is unknown if sex, as a variable, plays a role in the impact that HSP70 has upon vascular contraction. To narrow this gap, we investigated if differences exist in the expression levels of HSP70 in the aorta, and if targeting this protein contributes to sex disparity in vascular responses. We report that, compared with male animals, female rats present a reduction in the basal levels of HSP70. More compelling, we found that the blockade of HSP70 has a greater impact on phenylephrine-induced phasic and tonic vascular contraction in female animals. In fact, it seems that the inhibition of HSP70 significantly affects vascular Ca2+ handling mechanisms in females, which could be associated with the fact that these animals have impaired HSP70 expression. Corroborating this idea, we uncovered that the higher sensitivity of female rats to HSP70 inhibition does not involve an increase in NO-dependent vasodilation nor a decrease in vascular oxidative stress. In summary, our findings reveal a novel mechanism associated with sex-specific differences in vascular responses to α-1 adrenergic stimulation, which might contribute to unraveling the network of intertwined pathways conferring female protection to (cardio)vascular diseases.
Improvement of Gold Nanorods in Photothermal Therapy: Recent Progress and Perspective

Cancer is a life-threatening disease, and there is a significant need for novel technologies to treat cancer with an effective outcome and low toxicity. Photothermal therapy (PTT) is a noninvasive therapeutic tool that transports nanomaterials into tumors, absorbing light energy and converting it into heat, thus killing tumor cells. Gold nanorods (GNRs) have attracted widespread attention in recent years due to their unique optical and electronic properties and potential applications in biological imaging, molecular detection, and drug delivery, especially in the PTT of cancer and other diseases. This review summarizes the recent progress in the synthesis methods and surface functionalization of GNRs for PTT. The current major synthetic methods of GNRs and recently improved measures to reduce toxicity, increase yield, and control particle size and shape are first introduced, followed by various surface functionalization approaches to construct a controlled drug release system, increase cell uptake, and improve pharmacokinetics and tumor-targeting effect, thus enhancing the photothermal effect of killing the tumor. Finally, a brief outlook for the future development of GNRs modification and functionalization in PTT is proposed.
Modeling Transit Flow Through Port Gates and Connecting Channel in Baltic Sea—Liepaja Port—Liepaja Lake System

This study investigates a water transport features by extending Copernicus Marine Environment Service (CMEMS) to the Liepaja coast-port-channel-lake system with a two-way nested model. The Liepaja lake and Liepaja port are connected by Trade channel. The Liepaja port has three gates—the openings in wave breakers connecting the port aquatory with the Baltic sea. Each of gates has a corresponding dredged channel for securing the navigation. A hydrodynamic model is set up to study the flow and water level in this system. The area of the port gates, port and Trade channel are resolved by 33 m grid. The model results are verified against currents and sea level observations inside/outside port, Trade channel and Liepaja lake. Results and observations show that strong currents occur in the Trade channel in case of rapid sea level change in Baltic sea despite the Trade channel is rather shallow at the connection with Liepaja lake. The northern part of the Liepaja lake gets filled with brackish water during storm surge events. The channel has notable alternating current also during a relatively calm weather due to the port seiches. Long and narrow shape of the channel implies the Helmholtz type oscillations between the lake and the port with a period in approximately semidiurnal range. Hydrodynamic simulations describe well these oscillations but the phase of hourly scale oscillations in the port may differ in case of weak external forcing. Water exchange is significantly increased by the transit (gate to gate) sea currents. This transit flow usually occurs between South or Central gate and the North gate carrying sea water into the port. Northward flow of the surface layer is more characteristic in the port aquatory due the prevailing south-western winds. There are intense morphological processes at the coastline and underwater slope near the Liepaja port due to a sandy western coastline of Latvia, long fetch of the waves and strong currents at the port gates. Liepaja port is one of the Latvian ports in HywasPort operational service of hydrodynamics, waves and siltation.
Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.
PyGeNN: A Python Library for GPU-Enhanced Neural Networks

More than half of the Top 10 supercomputing sites worldwide use GPU accelerators and they are becoming ubiquitous in workstations and edge computing devices. GeNN is a C++ library for generating efficient spiking neural network simulation code for GPUs. However, until now, the full flexibility of GeNN could only be harnessed by writing model descriptions and simulation code in C++. Here we present PyGeNN, a Python package which exposes all of GeNN's functionality to Python with minimal overhead. This provides an alternative, arguably more user-friendly, way of using GeNN and allows modelers to use GeNN within the growing Python-based machine learning and computational neuroscience ecosystems. In addition, we demonstrate that, in both Python and C++ GeNN simulations, the overheads of recording spiking data can strongly affect runtimes and show how a new spike recording system can reduce these overheads by up to 10×. Using the new recording system, we demonstrate that by using PyGeNN on a modern GPU, we can simulate a full-scale model of a cortical column faster even than real-time neuromorphic systems. Finally, we show that long simulations of a smaller model with complex stimuli and a custom three-factor learning rule defined in PyGeNN can be simulated almost two orders of magnitude faster than real-time.
Exploring the Kairomone-Based Foraging Behaviour of Natural Enemies to Enhance Biological Control: A Review

Kairomones are chemical signals that mediate interspecific interactions beneficial to organisms that detect the cues. These attractants can be individual compounds or mixtures of herbivore-induced plant volatiles (HIPVs) or herbivore chemicals such as pheromones, i.e., chemicals mediating intraspecific communication between herbivores. Natural enemies eavesdrop on kairomones during their foraging behaviour, i.e., location of oviposition sites and feeding resources in nature. Kairomone mixtures are likely to elicit stronger olfactory responses in natural enemies than single kairomones. Kairomone-based lures are used to enhance biological control strategies via the attraction and retention of natural enemies to reduce insect pest populations and crop damage in an environmentally friendly way. In this review, we focus on ways to improve the efficiency of kairomone use in crop fields. First, we highlight kairomone sources in tri-trophic systems and discuss how these attractants are used by natural enemies searching for hosts or prey. Then we summarise examples of field application of kairomones (pheromones vs. HIPVs) in recruiting natural enemies. We highlight the need for future field studies to focus on the application of kairomone blends rather than single kairomones which currently dominate the literature on field attractants for natural enemies. We further discuss ways for improving kairomone use through attract and reward technique, olfactory associative learning, and optimisation of kairomone lure formulations. Finally, we discuss why the effectiveness of kairomone use for enhancing biological control strategies should move from demonstration of increase in the number of attracted natural enemies, to reducing pest populations and crop damage below economic threshold levels and increasing crop yield.
A Combined Prediction Model for Lymph Node Metastasis Based on a Molecular Panel and Clinicopathological Factors in Oral Squamous Cell Carcinoma

Objective Lymph node metastasis is the most important factor influencing the prognosis of oral squamous cell carcinoma (OSCC) patients. However, there is no proper method for predicting lymph node metastasis. This study aimed to construct and validate a preoperative prediction model for lymph node metastasis and guide personalized neck management based on the gene expression profile and clinicopathological parameters of OSCC. Methods Based on a previous study of related genes in OSCC, the mRNA expression of candidate genes was evaluated by real-time PCR in OSCC specimens. In this retrospective study, the gene expression profile and clinicopathological parameters of 112 OSCC patients were combined to construct the best prediction model for lymph node metastasis of OSCC. The model was validated with 95 OSCC samples in this study. Logistic regression analysis was used. The area under the curve (AUC) ultimately determined the diagnostic value of the prediction model. Results The two genes CDKN2A + PLAU were closely related to lymph node metastasis of oral squamous cell carcinoma. The model with the combination of CDKN2A, PLAU, T stage and pathological grade was the best in predicting lymph node metastasis (AUC = 0.807, 95% CI: 0.713-0.881, P=0.0001). The prediction model had a specificity of 96% and sensitivity of 72.73% for stage T1 and T2 OSCC (AUC = 0.855, 95% CI: 0.697-0.949, P=0.0001). Conclusions High expression of CDKN2A and PLAU was associated with lymph node metastasis in OSCC. The prediction model including CDKN2A, PLAU, T stage and pathological grade can be used as the best diagnostic model for lymph node metastasis in OSCC.
Bacterial Manipulation of the Integrated Stress Response: A New Perspective on Infection

Host immune activation forms a vital line of defence against bacterial pathogenicity. However, just as hosts have evolved immune responses, bacteria have developed means to escape, hijack and subvert these responses to promote survival. In recent years, a highly conserved group of signalling cascades within the host, collectively termed the integrated stress response (ISR), have become increasingly implicated in immune activation during bacterial infection. Activation of the ISR leads to a complex web of cellular reprogramming, which ultimately results in the paradoxical outcomes of either cellular homeostasis or cell death. Therefore, any pathogen with means to manipulate this pathway could induce a range of cellular outcomes and benefit from favourable conditions for long-term survival and replication. This review aims to outline what is currently known about bacterial manipulation of the ISR and present key hypotheses highlighting areas for future research.
Coiled-Coil Domain-Containing 68 Downregulation Promotes Colorectal Cancer Cell Growth by Inhibiting ITCH-Mediated CDK4 Degradation

Coiled-coil domain-containing 68 (CCDC68) plays different roles in cancer and is predicted as a tumor suppressor in human colorectal cancer (CRC). However, the specific role of CCDC68 in CRC and the underlying mechanisms remain unknown. Here, we showed that CCDC68 expression was lower in CRC than that in corresponding normal tissues, and CCDC68 level was positively correlated with disease-free survival. Ectopic expression of CCDC68 decreased CRC cell proliferation in vitro and suppressed the growth of CRC xenograft tumors in vivo. CCDC68 caused G0/G1 cell cycle arrest, downregulated CDK4, and upregulated ITCH, the E3 ubiquitin ligase responsible for CDK4 protein degradation. This increased CDK4 degradation, which decreased CDK4 protein levels and inhibited CRC tumor growth. Collectively, the present results identify a novel CDK4 regulatory axis consisting of CCDC68 and ITCH, which suggest that CCDC68 is a promising target for the treatment of CRC.
Evaluating temperature variations, energy fluctuations, and pathways when excitons are transferred in the FMO complex

ABSTRACT The Fenna-Matthews-Olson protein of green sulphur bacteria is a model photosynthetic unit. Therein, coupled chromophores transfer energy from antennae to a reaction centre. Close to unit quantum yield efficiencies have been recorded and wave-like transfer also observed in the Fenna-Matthews-Olson complex. The protein also possesses a relatively simple structure. These features have meant that the complex has been the subject of many investigations. In this thesis, primary aims are the assessment of how dynamics in a Fenna-Matthews-Olson monomer are affected by a change in temperature and explicit inclusion (or exclusion) of intra-molecular vibrational modes following a characterization of important values. A genetic type algorithm was crucial in parameterizing equations of motion that described energy transfer. Excellent fits to Fenna-Matthews-Olson spectra at 77 K were obtained by this evolutionary algorithm, so confidence could be expressed in any subsequent results. In the absence of explicitly including vibrational modes, it is proposed that transport of excitation populations is greatly conditioned by fine details in the density of state distributions for excitonic states. Non-trivial energy transfer was observed in one set of simulations; occupation of low-energy states was clearly enhanced at physiological temperatures relative to low temperature. The ‘skew’ of energy distributions, entering into equations of motion via a random sampling technique, are seen as being crucial and these affect resonance conditions in trajectories. When non-resonant vibrational modes were explicitly included in simulations, inter-state beatings were strong although energy transfer was not perceptibly enhanced relative to when only resonant modes were incorporated. Finally, pathways through the complex depended on low energy states with transfer towards the reaction centre inhibited if chromophores in the low to intermediate part of the excitonic spectrum had large resonance gaps.
INTENTION TOWARDS GREEN PRODUCTS CONSUMPTION FOR MILLENNIAL GENERATION

This study investigated the intention towards green products consumption for millennial generation. The aim of this study was to identify determinants of acceptance of green products, including attitude, subjective norm and perceived behavioral control of theory of planned behavior with gender as moderation variable towards purchase intention of green products. The sample was millennial generation with 100 responses around Purwokerto and we test this hypothesis performing a regression analysis. The findings revealed a significant effect of attitude, subjective norm and perceived behavioral control towards purchase intention. Gender did not moderate the effect of attitude, subjective norm, perceived behavioral control towards purchase intention.
83 GHz Microstrip Patch Antenna for Millimeter Wave Applications

In this paper, a rectangular microstrip patch antenna was designed for 5G high speed data transmission in the Millimeter-Wave (mm-wave) range from 75 to 110 GHz (W-band). The proposed design is a single element microstrip antenna that has an overall size of $2.02 \times 2.328\times 0.149\mathrm{mm}^{3}$. The radiating element having a dimension of $1.1483\times 1.429\mathrm{mm}^{2}$ is printed on a substrate material (RT/Duroid5880) with a height of 0.149 mm. Inset feed technique is used for matching between the feeding microstrip line and the rectangular radiating patch. The antenna provides a fractional bandwidth of 3.76% (about 3.12 GHz) and covers the range from 81.3717 GHz to 84.4912 GHz centered at 83 GHz. Further, the return loss, VSWR, gain and directivity achieved are 55.7937 dB, 1.0033, 7.9087 dBi and 7.8914 dB respectively. Good impedance matching, bandwidth, radiation characteristics at the operating band along with compact and small size confirm that the proposed antenna is suitable for 5G mm-wave applications.
Observers for High-Speed Sensorless PMSM Drives: Design Methods, Tuning Challenges and Future Trends

Rotor position and speed estimation methods are consolidated to reduce the cost and volume of permanent magnet synchronous motor (PMSM) sensorless control drives while maintaining high performance. Advanced nonlinear algorithms require accurate design for precise estimation tracking throughout the entire PMSM operation range. This paper presents a broad review of the main high-speed estimation methods for sensorless PMSM drives. First, the stability constraints and design methodologies of the main estimation techniques presented in the literature are discussed. In the second part, it is investigated the new observer design trends, which are used under non-ideal conditions, such as robustness to distortions, the effects of parameter variation, sensorless parameter estimation, and low sampling-frequency-to-speed ratio operation. Future trends on the design of observers for high-speed sensorless PMSM drives are also discussed.
An Enhanced Frequency-Adaptive Single-Phase Grid Synchronization Technique

Second-order generalized integrator phase-locked loop (SOGIPLL) relies on the frequency feedback from the PLL estimation. The frequency is used to generate the equivalent in-phase and in-quadrature signals of the single-phase grid voltage. The phase-angle-coupled frequency feedback makes the SOGIPLL structure vulnerable to various grid voltage transients such as voltage sag, phase-angle jumps (PAJs), and frequency variations. This article proposes a PLL independent frequency estimation technique. It estimates the frequency using the simplified teager energy operation on the normalized in-phase voltage component output of the SOGI. The phase-angle is estimated using the third-order polynomial approximated arctangent function on the in-phase and in-quadrature outputs of the proposed frequency-adaptive SOGI. The grid synchronization technique presented in this work avoids the use of any trigonometric operations and PLL gain tuning issues. Additionally, the impact of steady-state grid disturbances, namely, harmonics and dc offset on the proposed synchronization technique is investigated. Finally, its performance robustness is compared with other techniques during both grid transients and steady-state disturbances using both simulation analysis and experimental validation.
Machine learning based network intrusion detection for data streaming IoT applications

In recent years, Internet of Things (IoT) technologies have been widely used in many fields such as surveillance, health-care, smart metering and environment monitoring. This extensive usage leads to massive data management and a complexity in data analysis. A huge number of IoT sensors are deployed for monitoring task and send continuously their collected data to gateways. IoT applications are analyzing these data flows and making real time decisions about specific monitored events (fire, flood, terrorist attacks, etc.). Anomalies that may be related to sensor failures or network intrusions are affecting such decisions. Therefore, they should be detected and eliminated as soon as they arrive. This task requires real time data processing detectors for making accurate and fast predictions. In this paper, we design an architecture for a real time network intrusion detection system for IoT streaming data. The system was developed, deployed and tested with the two leading stream processing frameworks (Apache Flink and Apache Spark Streaming). We used two different public datasets and different machine learning algorithms. Results show considerable throughputs and high detection accuracy especially for Apache Flink.