Miracle-dz/UniFAR-FTUs huggingface dataset:
Neosetophomone B induces apoptosis in multiple myeloma cells via targeting of AKT/SKP2 signaling pathway

Multiple myeloma (MM) is a hematologic malignancy associated with malignant plasma cell proliferation in the bone marrow. Despite the available treatments, drug resistance and adverse side effects pose significant challenges, underscoring the need for alternative therapeutic strategies. Natural products, like the fungal metabolite neosetophomone B (NSP‐B), have emerged as potential therapeutic agents due to their bioactive properties. Our study investigated NSP‐B's antitumor effects on MM cell lines (U266 and RPMI8226) and the involved molecular mechanisms. NSP‐B demonstrated significant growth inhibition and apoptotic induction, triggered by reduced AKT activation and downregulation of the inhibitors of apoptotic proteins and S‐phase kinase protein. This was accompanied by an upregulation of p21Kip1 and p27Cip1 and an elevated Bax/BCL2 ratio, culminating in caspase‐dependent apoptosis. Interestingly, NSP‐B also enhanced the cytotoxicity of bortezomib (BTZ), an existing MM treatment. Overall, our findings demonstrated that NSP‐B induces caspase‐dependent apoptosis, increases cell damage, and suppresses MM cell proliferation while improving the cytotoxic impact of BTZ. These findings suggest that NSP‐B can be used alone or in combination with other medicines to treat MM, highlighting its importance as a promising phytoconstituent in cancer therapy.
New Disease Reports

Figure 4 Chilli, Capsicum annum, is an annual herbaceous vegetable and spice grown in almost all the states of India. Anthracnose (fruit rot) of chilli caused by Colletotrichum species results in both preand post-harvest fruit decay with yield losses of up to 50% (Pakdeevaraporn et al., 2005). The different species of Colletotrichum causing chilli anthracnose reported from India include: Colletotrichum acutatum, C. capsici, C. coccodes, C. dematium, C. gloeosporioides and C. siamense. In August 2014, chilli fruits showing typical anthracnose symptoms of sunken necrotic tissues with concentric brown black rings of acervuli were obtained from a farmer’s field in Jalna, Maharashtra, India.
DO-SA&R: Distant Object Augmented Set Abstraction and Regression for Point-Based 3D Object Detection

Point-based 3D detection approaches usually suffer from the severe point sampling imbalance problem between foreground and background. We observe that prior works have attempted to alleviate this imbalance by emphasizing foreground sampling. However, even adequate foreground sampling may be extremely unbalanced between nearby and distant objects, yielding unsatisfactory performance in detecting distant objects. To tackle this issue, this paper first proposes a novel method named Distant Object Augmented Set Abstraction and Regression (DO-SA&R) to enhance distant object detection, which is vital for the timely response of decision-making systems like autonomous driving. Technically, our approach first designs DO-SA with novel distant object augmented farthest point sampling (DO-FPS) to emphasize sampling on distant objects by leveraging both object-dependent and depth-dependent information. Then, we propose distant object augmented regression to reweight all the instance boxes for strengthening regression training on distant objects. In practice, the proposed DO-SA&R can be easily embedded into the existing modules, yielding consistent performance improvements, especially on detecting distant objects. Extensive experiments are conducted on the popular KITTI, nuScenes and Waymo datasets, and DO-SA&R demonstrates superior performance, especially for distant object detection. Our code is available at https://github.com/mikasa3lili/DO-SAR.
A preliminary investigation of canine heartworm infection rates in Western North Carolina

Abstract Dirofilaria immitis, more commonly known as canine heartworm, is a parasitic nematode that leads to heartworm disease in dogs. Adult heartworms occupy the heart and lungs of dogs and prevent these organs from functioning properly. Anecdotal evidence suggested that several counties in Western North Carolina showed a trend of heartworm incidence rates that were lower than the incidence rates of both North Carolina and the United States. This study looked to investigate this anecdotal trend using data from the Companion Animal Parasite Council. Public data confirm that both Madison and Buncombe County have lower rates of heartworm infection than North Carolina as a whole. In order to help explain this trend we conducted a pilot survey using Buncombe County as a model of mosquito species diversity in Western North Carolina. We trapped mosquitoes at three sites in Buncombe County from July 2020 to October 2020. Our mosquito diversity estimate was used to predict expected rates of heartworm prevalence and found a substantial difference between the actual and expected heartworm prevalence in Buncombe County. It is possible that factors other than mosquito diversity contribute to low heartworm prevalence in Buncombe County and in other counties in Western North Carolina.
Automatic computation of power grid objects using object-oriented modeling and abstract rule syntax

Smart grid scheduling automation system has a high demand for system computation capability, the existing system has no automatic computation and storage means for grid-level real-time monitoring objects, and the model construction and automatic computation of grid monitoring object computation and analysis results need to rely on a large number of heavy manual maintenance, which cannot satisfy the demand of the gradual expansion of the grid scale. In this paper, an automatic calculation method based on abstract rule syntax for grid objects is proposed. Firstly, the calculation logic and the calculation result storage model are designed according to the principle of object-oriented modeling, so that the calculation data have the basis for modeling; secondly, an abstract rule syntax is designed to support the electronic description of the calculation logic; next, a resolution algorithm for the abstract description of the object calculation rules is discussed and the common statistical algorithms for grid objects are cited to verify the calculation results of the grid objects; finally, the parsing algorithm for the abstract description of object computation rules is discussed, and the common statistical algorithms of power grid to verify the completeness and feasibility of the abstract rule grammar are cited.
Research on the application of enterprise big data mining based on deep neural network

With the rapid development of the Internet, the health big data industry is highly valued by society and the state, and how to effectively mine the large amount of health data generated by medical enterprises has become a current research hotspot. Neural networks, as a new network technology, can be well combined with health big data, and thus promote the deep mining of health big data. Therefore, based on the analysis of the characteristics of Convolutional Neural Networks (CNN), the study combines it with the Apriori algorithm and constructs a CNN-Apriori based enterprise big data mining model. The results show that the CNN-Apriori model takes 0.813s to converge to a steady state and has a fitting accuracy of 96.012% at a running time of 0.900s. In the mining of factors related to users' physical fitness, the users' physical fitness score improved most significantly from 90.023 to 96.321, and all the users' physical fitness scores obtained by the method improved to above 90. This shows that the proposed CNN-Apriori model can effectively mine the factors related to users' physical quality, which has good application effect in the big data mining of medical enterprises and has certain promotion value.
Research on autonomous intelligent inspection technology of substation based on image recognition and computer vision

Substation is the core hub of power grid transmission, which provides the necessary guarantee for the safe and stable operation of substation and even the whole power grid. There are many kinds of electrical equipment in substation, and the distribution is dense. Different equipment shows obvious differences in appearance and state. When inspecting this electrical equipment, we can not only use traditional manual inspection methods, but also rely on artificial intelligence technology and big data technology to build an intelligent inspection robot and realize the recognition of image information through image recognition technology. In this article, the application of intelligent inspection robot in autonomous intelligent inspection of substation is studied, and the path planning model of inspection robot is constructed by combining image recognition and computer vision technology. The experimental results show that the path planning algorithm of inspection robot in this article has small error and excellent accuracy, which can reach more than 98% after many iterations. The algorithm realizes the stable, reliable and high-precision inspection operation of the substation inspection robot.
Research on path configuration strategy of in-band network telemetry detection based on adaptive algorithm and P4

Traditional network measurement methods can't obtain fine-grained real-time network state information, and traditional network devices are not programmable. Therefore, in order to realize network-wide detection, it is necessary to design a high-level mechanism to configure multiple INT (In-band Network Telemetry) detection paths. In order to solve the above problems, this paper studies the INT detection path configuration strategy based on adaptive algorithm and P4. In this paper, an optimal detection path configuration algorithm is proposed. In the IP layer, an adaptive algorithm is proposed to predict the traffic trend and adjust the Sel-INT (selective INT) sampling rate. At the same time, in the EON (elastic optical network) layer, the out-of-band optical link polling is decided by using INT, and P4 is used to check the consistency of data plane forwarding behavior. The research results show that, when the number of switching nodes on the forwarding path is the same, the single-path verification time of P4 in different network topologies is basically the same, and the error range is less than 3%. For a given topology diagram, the more vertices in the set. Therefore, correspondingly, the algorithm execution time has a certain decline. The simulation results verify the effectiveness of the INT detection path configuration strategy, and they all have a certain guiding role in the actual deployment.
Long-COVID is Associated with Impaired Red Blood Cell Function

Abstract COVID-19 disease, caused by the severe acute respiratory syndrome virus 2 (SARS-CoV-2), induces a broad spectrum of clinical symptoms ranging from asymptomatic cases to fatal outcomes. About 10–35% of all COVID-19 patients, even those with mild COVID-19 symptoms, continue to show symptoms, i. e. , fatigue, shortness of breath, cough, and cognitive dysfunction, after initial recovery. Previously, we and others identified red blood cell precursors as a direct target of SARS-CoV-2 and suggested that SARS-CoV-2 induces dysregulation in hemoglobin- and iron-metabolism contributing to the severe systemic course of COVID-19. Here, we put particular emphasis on differences in parameters of clinical blood gas analysis and hematological parameters of more than 20 healthy and Long-COVID patients, respectively. Long-COVID patients showed impaired oxygen binding to hemoglobin with concomitant increase in carbon monoxide binding. Hand in hand with decreased plasma iron concentration and transferrin saturation, mean corpuscular hemoglobin was elevated in Long-COVID patients compared to healthy donors suggesting a potential compensatory mechanism. Although blood pH was within the physiological range in both groups, base excess- and bicarbonate values were significantly lower in Long-COVID patients. Furthermore, Long-COVID patients displayed reduced lymphocyte levels. The clinical relevance of these findings, e. g. , as a cause of chronic immunodeficiency, remains to be investigated in future studies. In conclusion, our data suggest impaired erythrocyte functionality in Long-COVID patients, leading to diminished oxygen supply. This in turn could be an explanation for the CFS, dyspnea and anemia. Further investigations are necessary to identify the underlying pathomechanisms.
Assessment of ChatGPT in the Prehospital Management of Ophthalmological Emergencies – An Analysis of 10 Fictional Case Vignettes

Abstract Background The artificial intelligence (AI)-based platform ChatGPT (Chat Generative Pre-Trained Transformer, OpenAI LP, San Francisco, CA, USA) has gained impressive popularity in recent months. Its performance on case vignettes of general medical (non-ophthalmological) emergencies has been assessed – with very encouraging results. The purpose of this study was to assess the performance of ChatGPT on ophthalmological emergency case vignettes in terms of the main outcome measures triage accuracy, appropriateness of recommended prehospital measures, and overall potential to inflict harm to the user/patient. Methods We wrote ten short, fictional case vignettes describing different acute ophthalmological symptoms. Each vignette was entered into ChatGPT five times with the same wording and following a standardized interaction pathway. The answers were analyzed following a systematic approach. Results We observed a triage accuracy of 93.6%. Most answers contained only appropriate recommendations for prehospital measures. However, an overall potential to inflict harm to users/patients was present in 32% of answers. Conclusion ChatGPT should presently not be used as a stand-alone primary source of information about acute ophthalmological symptoms. As AI continues to evolve, its safety and efficacy in the prehospital management of ophthalmological emergencies has to be reassessed regularly.
That's AWESOME: Awareness While Experiencing and Surfing On Movies through Emotions

This paper presents the AWESOME research project: Awareness While Experiencing and Surfing On Movies through Emotions, which focused on the role of emotions in movies, aiming to enhance viewers' emotional awareness while searching, accessing and watching movies. The project addressed challenges such as capturing viewers' emotional responses, and analyzing movie content based on subtitles, audio, and video. Through a multimethod approach and combining distinct measurements, the project gained insights into viewers' emotions, developed algorithms for estimating emotions, and created interactive tools to annotate and access movies based on emotions. User satisfaction with the tools provided further evidence about the relevance of taking emotions into account and providing an effective support. Through this paper, we present an overview about the motivations underlying the AWESOME project, its main goals, tasks and contributions, and highlight directions for future work.
EntryBleed: A Universal KASLR Bypass against KPTI on Linux

For years, attackers have compromised systems by developing exploits that rely on known locations of kernel code and data segments. KASLR (Kernel Address Space Layout Randomization) is a key mitigation in modern operating systems which hampers these attacks through runtime randomization of the kernel image base address. KPTI (Kernel Page Table Isolation) is another defense mechanism, originally introduced to defend against the 2018 Meltdown attack by unmapping kernel addresses during user code execution. This security mechanism makes it harder for attackers to leak kernel address mappings through micro-architectural side channels. However, a few pages for system call and interrupt handling were exempted from isolation for the sake of user to kernel context transitions. We present the EntryBleed vulnerability (CVE-2022-4543) as a universal bypass against the KASLR protection mechanism through a combination of micro-architectural side channels and design flaws in the KPTI mitigation on Intel CPUs. We demonstrate that the bug we identified can accurately de-randomize the kernel address space within a second on modern Intel CPUs in both physical host and hardware-accelerated virtual machine environments. We then provide a root cause analysis to locate the core micro-architectural behaviors that enable EntryBleed, both on physical and under virtualized environments. Furthermore, we propose a performant mitigation based closely upon a pre-existing KASLR hardening mechanism. If left unpatched, attackers will be able to easily bypass KASLR, greatly lowering the barrier for exploit development and increasing the risk of serious threats against the Linux operating system.
Technology Enhanced Mulsemedia Learning (TEML) for Learners with Dyslexia

Mulsemedia-multiple sensorial media- includes both traditional multimedia and human sensorial effects, which gives immersive real-world experience. The booming of new technology and wearable devices creates a new avenue for Mulsemedia research in education and has brought new opportunities for Technology Enhanced Mulsemedia Learning (TEML). Traditional methods focus on unisensory experiences, even though the surrounding environment is multisensory. Moreover, this type of learning helps to stimulate various sensory channels to reinforce the learning process, which can also more benefit individuals with a variety of learning disabilities particularly dyslexia which affects a person’s ability to read. Moreover, TEML helps to obtain 21st-century learning skills such as cognitive, productivity, social-cultural, metacognitive, and technological dimensions. This paper proposes TEML for learners with dyslexia, for that, this study focuses on an approach for identifying methods for dyslexia, a method for designing Mulsemedia devices using IoT technology, and an evaluation method for performing quality of experience (QoS) using questionnaire, and physiological signals such as GSR (Galvanic Skin Responses), and EEG (Electroencephalography). Those methods help to analyze the learner’s emotional responses while experiencing Mulsemedia content.
Unraveling Evolutionary Dynamics: Insights from In Silico Experiments on Selective Mechanisms in Controlled Environments

In this paper, we present a series of in silico experiments aimed at probing the evolutionary properties of our model. Our investigation encompasses multiple methodologies, beginning with the standard model used in population genetics for measuring natural selection. Next, we employ the Price equation, a well-established formalism known for its effectiveness in tracking how the relationship between parents and offspring evolves over time. In conclusion, we delve into the model results to explain, in the light of evolutionary theory, how the selective mechanism operates. Furthermore, the speculation about the mechanism will be hindered on the agents of the selective process. Even though natural selection can be considered as a statistical phenomenon, sprouting from the change in population frequencies, we argue that in models where there is a elevate control on the environment, it is possible to define the single element responsible for the selective pressure on the units of selection.
Piezo1 mutant zebrafish as a model of idiopathic scoliosis

Scoliosis is a condition where the spine curves sideways, unique to humans due to their upright posture. However, the cause of this disease is not well understood because it’s challenging to find a model for experimentation. This study aimed to create a model for human idiopathic scoliosis by manipulating the function of mechanosensitive channels called Piezo channels in zebrafish. Zebrafish were chosen because they experience similar biomechanical forces to humans, particularly in relation to the role of mechanical force in scoliosis progression. Here we describe piezo1 and piezo2a are involved in bone formation, with a double knockout resulting in congenital systemic malformations. However, an in-frame mutation of piezo1 led to fully penetrant juvenile-onset scoliosis, bone asymmetry, reduced tissue mineral density, and abnormal intervertebral discs-resembling non-congenital scoliosis symptoms in humans. These findings suggest that functional Piezo channels responding to mechanical forces are crucial for bone formation and maintaining spine integrity, providing insights into skeletal disorders.
Quantitative imaging of three-dimensional fiber orientation in the human brain via two illumination angles using polarization-sensitive optical coherence tomography

The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Optical imaging methods such as polarization-sensitive optical coherence tomography (PS-OCT) provide important tools to directly quantify fiber orientation at micrometer resolution. However, brain imaging based on the optic axis by PS-OCT so far has been limited to two-dimensional in-plane orientation, preventing the comprehensive study of connectivity in 3D. In this work, we present a novel method to obtain the 3D fiber orientation in full angular space with only two illumination angles. We measure the optic axis orientation and the apparent birefringence by PS-OCT from a normal and a 15 deg tilted illumination, and then apply a computational method yielding the 3D optic axis orientation and true birefringence. We verify that our method accurately recovers a large range of through-plane orientations from -85 deg to 85 deg with a high angular precision. We further present 3D fiber orientation maps of entire coronal sections of human cerebrum and brainstem with 10 μm in-plane resolution, revealing unprecedented details of fiber configurations. We envision that further development of our method will open a promising avenue towards large-scale 3D fiber axis mapping in the human brain and other complex fibrous tissues at microscopic level.
Determinação e avaliação quimiométrica da composição mineral de quiabo (Abelmoschus esculentus L.) comercializados na cidade de Salvador

In this work, the mineral content Ca, Cu, Fe, K, Mg, Mn, Na, P and Zn of okra (Abelmoschus esculentus L) marketed in the state of Bahia, of both conventional and organic cultivars was determined during May 2011 to April 2012. Were also assessed the effects on mineral composition after cooking process in water. The samples were digested in a heating block using nitric acid and hydrogen peroxide, and analysed using inductively coupled plasma optical emission spectrometry (ICP OES). The cooking procedure was performed in digesters tubes for 10 min after boiling water. The cooking water was disregarded and the vegetable was subjected to digestion. The accuracy of the method was confirmed using a standard reference material, tomato leaves (NIST 1573a), the coefficients of variation (in%), the limits of detection (LOD) and quantification (LQ). The results were evaluated using multivariate analysis. The exploratory analysis performed with use of chemometric tools PCA and HCA, has relevant information as to the seasonal variation of the samples and to evaluate the effect of baking in the minerals present in the okra. The elements Na, Mg, P, Mn and Cu presented a low variation in the periods of December 2011 to April 2012. While Fe and Zn showed higher concentrations in the period of may/jun 2011 and dec/jan 2012. Ca and K showed the highest concentrations in the mar/Apr 2011. It has not been possible to discriminate the samples and to variations in the forms of cultivation (organic or conventional) and regional (cities), because no group among the data was observed in PC 's. Raw and cooked samples tended to separate in principal component analysis (PCA) and hierarchical cluster analysis (HCA). The average percentage reductions after cooking ranged from 60 to 23% for micro-nutrients (Cu, Fe, Mn and Zn), and 63 to 11% for the macronutrients (Ca, K, Mg, Na and P), and K the element that showed the highest reduction.