Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel dissymmetric copper bis(thiosemicarbazone) complexes for medical diagnostic imaging by positron emission tomography

Bis(thiosemicarbazone) ligand derivatives and their metal complexes have long been of interest as they have applications as anticonvulsants, super-oxide dismutase-like radical scavengers, in the investigation of Alzheimer’s disease and diagnostic imaging. Copper (II) bis(thiosemicarbazone) derivatives have been used extensively in the imaging of oxygen deficient (hypoxic) cells for the detection and imaging of cancerous tissues and heart disease via Positron Emission Tomography (PET). It is possible to fine tune the bis(thiosemicarbazone) complexes redox potentials and lipophilicity by altering the substituents on the Q1 and Q2 position and the R1, R2, R3 and R4 locations respectively. To date only symmetric bis(thiosemicarbazone) ligands (R1=R3, R2=R4) have been evaluated for hypoxia imaging. This thesis reports the synthesis of dissymmetric ligands (R1? R3, R2? R4) in order to gain further control of the properties of the complexes and therefore the locations they will migrate to. A range of ligands has also been synthesised for the monitoring of copper metabolism within the brain for the investigation of Alzheimer’s disease and other neurodegenerative disorders. Ligand synthesis has been achieved by controlling the condensation reactions between dicarbonyl compounds and 4-substituted-3-thiosemicarbazides. Synthesis via an alternative acetal protecting method has also been investigated. Thirty bis(thiosemicarbazone) ligands have been successfully synthesised, of which thirteen are symmetric and seventeen dissymmetric. From this library of ligands, eighteen copper complexes have been synthesised along with twenty zinc complexes. The zinc complexes have the potential to act as convenient precursors for the rapid synthesis of radio-copper complexes via a transmetalation method. All ligands, complexes and intermediates have been fully characterised by a range of techniques including IR spectroscopy, Raman spectroscopy, NMR spectroscopy, UV-vis spectroscopy, elemental analysis and mass spectroscopy. A new cyclic by-product from the ligand synthesis has also been isolated and fully characterised.
The Interleukin 1 receptor antagonist is a direct target gene of PPAR  in

Background/Aims: The Peroxisome Proliferator-Activated Receptor (PPAR)  belongs to the superfamily of Nuclear Receptors and plays an important role in numerous cellular processes, including lipid metabolism. It is known that PPAR  also has an anti-inflammatory effect, which is mainly achieved by down-regulating pro-inflammatory genes. The objective of this study was to further characterize the role of PPAR  in inflammatory gene regulation in liver. Results: According to Affymetrix micro-array analysis, the expression of various inflammatory genes in liver was decreased by treatment of mice with the synthetic PPAR  agonist Wy14643 in a PPAR  -dependent manner. In contrast, expression of Interleukin-1 receptor antagonist (IL-1ra), which was acutely stimulated by LPS treatment, was induced by PPAR  . Up-regulation of IL-1ra by LPS was lower in PPAR  -/- mice compared to Wt mice. Transactivation and chromatin immunoprecipitation studies identified IL-1ra as a direct positive target gene of PPAR  with a functional PPRE present in the promoter. Upregulation of IL-1ra by PPAR  was conserved in human HepG2 hepatoma cells and the human monocyte/macrophage THP-1 cell line. Conclusions: In addition to down-regulating expression of pro-inflammatory genes, PPAR  suppresses the inflammatory
Strategie zur Gewinnung Neuronaler Modelle nichtlineare

Zur Systemanalyse oder Reglersynthese ist in den meisten Fallen ein Modell der zu untersuchenden Strecke erforderlich. Dazu ist zunachst eine moglicherweise aufwendige theoretische Modellbildung notwendig. Es hat sich gezeigt, da K unstliche Neuronale Netze dazu geeignet sind, nichtlineare dynamische Systeme ohne vorherige theoretische Modellbildung zu identi zieren. Die Schwierigkeit liegt jedoch zunachst darin, die f ur das Modell notwendigen Informationen  uber die anzusetzende Modellstruktur zu ermitteln. Im ersten Teil des Beitrags wird eine neue Methode vorgestellt, mit der die notwendigen Verzogerungen der Einund Ausgangsgro e, welche die Dynamik charakterisieren,  uber die Schatzung des Gradientenvektors der Systemnichtlinearitat ermittelt werden konnen. Ist die Modellstruktur bestimmt, kann das nichtlineare dynamische Modell durch numerische Optimierung ermittelt werden. Aufgrund des Approximationsfehlers des Neuronalen Netzes und der verzogerten R uckkopplung der Ausgangsgro e stellt sich eine dynamische Fehlerfortp anzung ein, die zu gro eren Abweichungen zwischen Systemund Modellverhalten f uhren kann. Daher wird im zweiten Teil ein iteratives Verfahren vorgeschlagen, bei dem weitere Trainingsdaten gezielt an den Stellen des Eingangsraumes hinzugef ugt werden, an denen eine Modellverbesserung notwendig ist. Abschlie end wird die Methode zur Modelloptimierung am Beispiel eines R uhrkesselreaktors veranschaulicht.
Dynamic Characteristics of State Transitions Composed of Neural Activity in the Brain by Circadian Rhythms

In recent years, as a treatment for mental disorders in addition to drug treatment, a non-drug treatment called chronotherapy has been attracting attention. However, the achievement of optimized chronotherapy for each subject's condition requires that the disturbance of the patient's circadian rhythm must be captured over a long duration. Therefore, it is necessary to develop biomarkers that are easy to measure, quantitative, and continuously measured. Complexity analysis of electroencephalograms revealed specific patterns related to circadian rhythms. However, such complexity analysis cannot capture variability in spatial patterns, although moment-to-moment temporal dynamic characteristics can be captured. Therefore, it is necessary to evaluate the dynamic characteristics of the interaction of neural activity throughout the brain. To evaluate the dynamic whole-brain interaction, we proposed a new microstate approach based on the instantaneous frequency distribution. In this context, we hypothesized that it would be possible to detect circadian rhythms using the microstate approach. In this study, to clarify the dynamic interactions of the entire neural network of the brain by circadian rhythms, we measured EEG data at day and night, and detected dynamic state transitions based on the instantaneous frequency distribution of the whole brain from EEG. The results showed the probability of transition among region-specific phase-leading states related to circadian rhythms. This finding might be widely utilized to detect circadian rhythms in healthy and pathological conditions.
The KOBE experiment: K-dwarfs Orbited By habitable Exoplanets. Project goals, target selection, and stellar characterization

The detection of habitable worlds is one of humanity's greatest endeavors. So far, astrobiological studies show that one of the most critical components for life development is liquid water. Its chemical properties and its capacity to dissolve and hence transport other substances makes this constituent a key piece in the development of life. As a consequence, looking for life as we know it is directly related to the search for liquid water. For a remote detection of life in distant planetary systems, this means looking for planets in the so-called habitable zone. In this sense, K-dwarf stars are the perfect hosts. Contrary to G-dwarfs, the habitable zone is closer, thus making planet detection easier using transit or radial velocity techniques. Contrary to M-dwarfs, the stellar activity is much smaller, hence having a smaller impact in both the detectability and in the true habitability of the planet. Also, K-dwarfs are the quietest in terms of oscillations, and granulation noise. Despite this, there is a dearth of planets in the habitable zone of K-dwarfs due to a lack of observing programs devoted to this parameter space. In response to a call for Legacy Programs of the Calar Alto observatory, we have started the first dedicated and systematic search for habitable planets around K-dwarfs, the K-dwarfs Orbited By habitable Exoplanets (KOBE). This survey is monitoring the radial velocity of 50 carefully pre-selected K-dwarfs with the CARMENES instrument along 5 semesters with an average of 90 data points per target. Based on planet occurrence rates convolved with our detectability limits, we expect to find $1.68\pm 0.25$ planets per star in the KOBE sample and in half of the sample we expect to find one of those planets within the habitable zone. In this paper, we describe the project motivation, goals and target selection and preliminary stellar characterization.
Multi Objective Task Offloading in Fog Computing using Sparrow Search algorithm

The Internet of Things (IoT) has become an important part of different applications such as smart homes, healthcare, agriculture, industries, and education. This dependency on IoT from different sectors leads to a large increase in the number of IoT devices. This increases the network congestion due to the large number of data re-quests generated from these devices. Therefore, IoT devices requires large amounts of computing resources. IoT devices offload tasks generated by IoT devices to cloud servers to overcome the problem of limited IoT devices. Offloading tasks to cloud servers overcomes the problem of limited oT devices but increases the response time because of high latency of the cloud. Fog computing is a new paradigm that is located between the cloud and IoT devices and provides low latency instead of high latency of cloud computing. Task offloading to fog nodes reduces response time effectively in a cloud-fog environment. Fog computing resources are no unlimited therefore, developing an effective offloading model to fog nodes is challenging. In this paper, using Spar-row Search Algorithm (SSA), an efficient Offloading Fog Algorithm (OFA-SSA) is proposed to offload tasks from IoT devices to fog nodes. The proposed task offloading model is introduced as a multi-objective optimization problem with the objective of minimizing the response time and cost. The proposed OFA-SSA is compared with the Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO) algorithm. The simulation results show that the proposed task offloading model using OFA-SSA improves the convergence and it outperforms the existing algorithms in minimizing the response time. The proposed OFA-SSA minimize the response time by 8% and 5% comparing to PSO and ACO algorithms.
Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leão Pinto Jr, Bayesian analysis of a multivariate null intercept error-in-variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763–771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161–178].
On the FP3‐Conjecture for Metabelian Groups

where both M and Q are abelian. G is uniquely given, up to isomorphism, by the abelian group Q, the Q-module M (by conjugation in G), and the cohomology class of the extension in H(Q,M). The group G is of type Fn, n > 0, if it admits a K(G, 1)-complex with finite n-skeleton. Every group is of type F0. The group G is of type F1 if and only if it is finitely generated, and of type F2 if and only if it is finitely presented. The metabelian groups constitute a class of groups G for which the property of being of type Fn relates in a very subtle way to the internal structure of G. The Fn-conjecture proposes a complete description of this relationship in terms of the Q-module M alone. We state its assertion in detail below. It requires the notion of the geometric invariant Σ(M) of a Q-module M was introduced in [5] to settle the case n = 2. The precise wording and first evidence of the Fn-conjecture for n > 2 came up only in [2]. The full conjecture is still an open question. The purpose of this paper is to prove it in the case when n = 3 and extension (1) splits. The extension G of M by Q is finitely generated if and only if M is finitely generated as a Q-module and Q is finitely generated. This obvious fact is all there is to the F1-conjecture, and we shall henceforth assume that G is finitely generated and n > 2. Moreover, the property that G be of type Fn is inherited and detected by subgroups of finite index. Hence we can pass to a subgroup of finite index and henceforth assume that Q is free abelian of finite rank m. We write Q as a multiplicative group, but we use an embedding of Q into R that identifies Q with the integral lattice of R and thus converts the multiplicative notion into an additive one. The Fn-conjecture requires some elementary geometry that is introduced via the support of group ring elements λ ∈ ZQ. If λ = ∑q∈Q nqq is the unique expansion of λ in the free abelian group ZQ, then we put supp (λ) = {q ∈ Q|nq 6= 0}.
CryptNoSQL – A Methodology for Secure Querying and Processing of Encrypted NoSQL Data on the Cloud Environment

- Businesses today use modern computing technologies such as Big Data and Machine Learning in their daily operations, which require effective management of large amounts of data. Relational data formats are no longer suitable for these applications, and NoSQL data formats are preferred. The use of cloud infrastructure offers advantages such as scalability, availability, and resource maintenance, but data security remains a challenge. Although cloud vendors provide encryption features, they may not be sufficient for sensitive data. To address this, some businesses use their encryption methods, but retrieving data from an encrypted form may not be possible. While specific encryption methods support the processing of encrypted data without decryption, there is no complete implementation of secure processing for NoSQL data from MongoDB or other databases. The proposed methodology, called CryptNoSQL, provides a secure way to query and process NoSQL data, including updates on encrypted data. We introduce a customized database design model that selects an appropriate encryption method during the insertion of a document based on the type of field and the operation it will be involved in. Our experimental results demonstrate that our approach is suitable for organizations with sensitive data hosted on the cloud and that require frequent query operations on this data.
Remote Sensing Imagine Change Detection Using Semi-Supervised Neural Networks

By comparing and contrasting photographs of the same location taken at various periods using remote sensing technology, we may determine whether or not there have been any changes to the land cover there. Identifying shifts in a timely manner will facilitate the identification of natural disasters and climate change, the evaluation of damage, and the rehabilitation of affected areas. Both supervised and unsupervised methods have been utilised historically for change detection. In practise, specialists can only gather a small number of tagged patterns to use in solving the change detection issue. In this case, supervised techniques are ineffective, and if unsupervised methods are employed instead, some scarce but useful labels are wasted. In this dissertation, we offer a novel methodology to enhance change detection in the face of data scarcity by combining a small number of labelled patterns with a large number of unlabeled ones. In this study, many neural network architectures are employed to develop active and semi-supervised learning change detection methods. For better change detection when only a small number of labelled patterns are available, we redesign two unsupervised learning based neural networks, a modified self-organizing feature map and a Hopfield type neural network, in a semi-supervised framework.
Interactive comment on “ Seasonal and diurnal variations of particulate nitrate and organic matter in the Central European atmospheric aerosol ” by L .

Response: We agree that it was not clearly explained that reactions R1 and R2 are not the only reactions affecting the ammonium nitrate equilibrium and that they were considered as a simplified and theoretical approach only. This is now clearly mentioned: “The philosophy of our approach was based on a comparison between measured nitrate concentration and theoretical equilibrium constants for pure ammonium nitrate (Kp* and Kdeliq). This is a simple and theoretical approach, considering only that ammonium nitrate is externally mixed, which is generally not the case, and that the gas-to-particle equilibrium is reached instantaneously”. Additionally, a discussion on the factors influencing the ammonium nitrate equilibrium was added on a specific subsection.
Commentary Focusing on the Glomerular Slit Diaphragm Podocin Enters the Picture

The nature of the glomerular filtration barrier is currently one of the most intensely studied and exciting problems in nephrology research, primarily because of the relatively recent discoveries that two novel and two previously known genes are intimately involved in glomerular filtration. The two novel genes, NPHS1 and NPHS2 (encoding nephrin and podocin, respectively) were identified by positional cloning. NPHS1 is mutated in congenital nephrotic syndrome of the Finnish type, 1 and NPHS2 is mutated in steroid resistant nephrotic syndrome. 2 The other two genes, previously characterized but not known to be involved in filtration, are ACTN4 , encoding (cid:2) -acti- nin-4, and Cd2ap , encoding CD2-associated protein (CD2AP). ACTN4 was found to be mutated in several families exhibiting inherited focal segmental glomerulo-sclerosis, 3 and knockout mice lacking CD2AP exhibit congenital nephrotic syndrome. 4 Several recent reviews have addressed the potential roles of these molecules in glomerular filtration. 5–10 All four of these genes have been shown to be expressed by podocytes, the specialized epithelial cells that lie atop the glomerular basement membrane (GBM) in the urinary space. Podocytes make up the final cell layer across which the glomerular ultrafiltrate must pass before flowing down the tubular portions of the nephron toward the ureter and bladder. Podocytes elaborate long, regularly spaced, interdigitated foot processes that en-wrap the glomerular capillaries. Near the GBM, the foot processes are connected by a thin structure termed the glomerular slit diaphragm. The existence of the slit has been known at an ultrastructural level for decades,
Transfer Learning-Based Intrusion Detection System for a Controller Area Network

The Controller Area Network (CAN) is a major protocol for in-vehicle network communications. This protocol is simple and efficient for message transmission and the smooth functioning of an in-vehicle system. On the other hand, the weaknesses of this protocol, such as the ID-based arbitration method for message transmission and lack of authentication mechanism, make it vulnerable to various security attacks, including DoS attacks, Fuzzy attacks, impersonation attacks, and replay attacks. Since there is no authentication mechanism for transmitted messages, we need a way to distinguish between normal and attack messages. An intrusion detection system (IDS) is an option for this problem because it can raise alarms when there are flaws in the system. IDS is very efficient for intrusion detection where messages with the same IDs are transmitted periodically. The deviation from the normal pattern of message transmission will force the IDS system to trigger alarms. Most studies on the CAN bus IDS system were based on a supervised learning approach. On the other hand, the lack of labeled datasets and a huge amount of training time make it inefficient for new attack patterns. This paper proposes a transfer learning-based IDS system for in-vehicle network intrusion detection. The extraction of quality features using transfer learning (TL) and appropriate fine-tuning methodology is used in the proposed model. This approach can use the available intrusion attack dataset to detect new attacks. The experimental results indicated that the proposed deep hybrid transfer learning (TL) model detects new threats with a high accuracy of approximately 99.9% when compared to state-of-the-art methods, while also lowering training and testing time by more than 30%.
Structural Styles in Rift Basins: Interpretation Methodology and Examples from Southeast Asia: ABSTRACT

The authors present graphical solutions to the extensional fault-related folding equations of Xiao and Suppe (1992), simplifying the prediction of normal fault location or rollover geometry from subsurface data. These equations also predict the extent of bed thinning and elongation in hanging wall strata. They have derived new equations that relate change in fault slip across a fault bend to fault geometry. Applying these equations in seismic interpretation makes it easier to (1) construct balanced cross-sections, (2) account for the slip observed, and (3) determine the growth history of extensional fault-related folds. They have applied these concepts to several southeast Asian rift basins in Malaysia, Myanmar, Indonesia, and Thailand. These basins were formed by early Tertiary crustal extension, producing rollover structures in which sediment supply generally did not keep up with subsidence. These under-filled, internally drained depressions periodically contained lakes, providing the environment for deposition of organic-rich strata that ultimately became hydrocarbon source rock. Typically, the main basin bounding faults dip 35-55[degrees] near their upper terminations and flatten to become subhorizontal. Synthetic and antithetic secondary faults are usually present. Late compaction faulting often propagates upward from major extensional faults and may reactivate the upper portions of these faults. In manymore » basins, late compression produced inversion structures. By applying the concepts of extensional fault-related folding to these basins, they can (1) explain observed geometries, (2) predict poorly imaged geometries, (3) predict the location of source and reservoir facies, and (4) determine the timing of faulting relative to deposition of source and reservoir rocks. « less