Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of PSD95‐nNOS protein–protein interactions decreases morphine reward and relapse vulnerability in rats

Glutamate signalling through the N‐methyl‐d‐aspartate receptor (NMDAR) activates the enzyme neuronal nitric oxide synthase (nNOS) to produce the signalling molecule nitric oxide (NO). We hypothesized that disruption of the protein–protein interaction between nNOS and the scaffolding protein postsynaptic density 95 kDa (PSD95) would block NMDAR‐dependent NO signalling and represent a viable therapeutic route to decrease opioid reward and relapse‐like behaviour without the unwanted side effects of NMDAR antagonists. We used a conditioned place preference (CPP) paradigm to evaluate the impact of two small‐molecule PSD95‐nNOS inhibitors, IC87201 and ZL006, on the rewarding effects of morphine. Both IC87201 and ZL006 blocked morphine‐induced CPP at doses that lacked intrinsic rewarding or aversive properties. Furthermore, in vivo fast‐scan cyclic voltammetry (FSCV) was used to ascertain the impact of ZL006 on morphine‐induced increases in dopamine (DA) efflux in the nucleus accumbens shell (NAc shell) evoked by electrical stimulation of the medial forebrain bundle (MFB). ZL006 attenuated morphine‐induced increases in DA efflux at a dose that did not have intrinsic effects on DA transmission. We also employed multiple intravenous drug self‐administration approaches to examine the impact of ZL006 on the reinforcing effects of morphine. Interestingly, ZL006 did not alter acquisition or maintenance of morphine self‐administration, but reduced lever pressing in a morphine relapse test after forced abstinence. Our results provide behavioural and neurochemical support for the hypothesis that inhibition of PSD95‐nNOS protein–protein interactions decreases morphine reward and relapse‐like behaviour, highlighting a previously unreported application for these novel therapeutics in the treatment of opioid addiction.
Advanced Persistent Threat Hacking: The Art and Science of Hacking Any Organization

Master the tactics and tools of the advanced persistent threat hacker In this book, IT security expert Tyler Wrightson reveals the mindset, skills, and effective attack vectors needed to compromise any target of choice. Advanced Persistent Threat Hacking discusses the strategic issues that make all organizations vulnerable and provides noteworthy empirical evidence. You'll learn a proven APT Hacker Methodology for systematically targeting and infiltrating an organization and its IT systems. A unique, five-phased tactical approach to APT hacking is presented with real-world examples and hands-on techniques you can use immediately to execute very effective attacks. Review empirical data from actual attacks conducted by unsophisticated and elite APT hackers alike Learn the APT Hacker Methodology--a systematic approach designed to ensure success, avoid failures, and minimize the risk of being caught Perform in-depth reconnaissance to build a comprehensive understanding of the target Obtain non-technical data about the target, including open source, human, financial, and geographical intelligence Use social engineering to compromise a specific system, application, or workstation Identify and attack wireless networks and wireless client devices Spearphish with hardware-based Trojan devices Physically infiltrate target facilities to obtain access to assets and compromise digital lily pads
Avogadro Number the 11 Dimensions Alternative

It is very clear that, to unify 2 interactions if 5 dimensions are required, for unifying 4 interactions 10 dimensions are required. For 3+1 dimensions if there exists 4 (observed) interactions, for 10 dimensions there may exist 10 (observable) interactions. To unify 10 interactions 20 dimensions are required. From this idea it can be suggested that- with `n' new dimensions `unification' problem can not be resolved. By implementing the gravitational constant in atomic and nuclear physics, independent of the CGS and SI units, Avogadro number can be obtained very easily and its order of magnitude is $\cong N \cong 6 \times 10^{23}$ but not $6 \times 10^{26}. $ If $M_P$ is the Planck mass and $m_e$ is the rest mass of electron, semi empirically it is observed that, $M_g \cong N^{\frac{2}{3}}\cdot \sqrt{M_Pm_e} \cong 1.0044118 \times 10^{-3} \; Kg. $ If $m_{p} $ is the rest mass of proton it is noticed that $\ln \sqrt{\frac{e^{2} }{4\pi \varepsilon _{0} Gm_{P}^{2} } } \cong \sqrt{\frac{m_{p} }{m_{e} } -\ln \left(N^{2} \right)}. $ Key conceptual link that connects the gravitational force and non-gravitational forces is - the classical force limit $\left(\frac{c^{4} }{G} \right)$. For mole number of particles, if strength of gravity is $\left(N.G\right),$ any one particle's weak force magnitude is $F_{W} \cong \frac{1}{N} \cdot \left(\frac{c^{4} }{N.G} \right)\cong \frac{c^{4} }{N^{2} G} $. Ratio of `classical force limit' and `weak force magnitude' is $N^{2} $. Assumed relation for strong force and weak force magnitudes is $\sqrt{\frac{F_{S} }{F_{W} } } \cong 2\pi \ln \left(N^{2} \right)$. From SUSY point of view, `integral charge quark fermion' and `integral charge quark boson' mass ratio is $\Psi=2.262218404$ but not unity. With these advanced concepts an ``alternative" to the `standard model' can be developed.
Cell-Type Dependent Regulation of the Electrogenic Na+/HCO3− Cotransporter 1 (NBCe1) by Hypoxia and Acidosis in Glioblastoma

Glioblastoma multiforme (GBM) is the most common and malignant brain tumour. It is characterised by transcriptionally distinct cell populations. In tumour cells, physiological pH gradients between the intracellular and extracellular compartments are reversed, compared to non-cancer cells. Intracellular pH in tumour cells is alkaline, whereas extracellular pH is acidic. Consequently, the function and/or expression of pH regulating transporters might be altered. Here, we investigated protein expression and regulation of the electrogenic sodium/bicarbonate cotransporter 1 (NBCe1) in mesenchymal (MES)-like hypoxia-dependent and -independent cells, as well as in astrocyte-like glioblastoma cells following chemical hypoxia, acidosis and elucidated putative underlying molecular pathways. Immunoblotting, immunocytochemistry, and intracellular pH recording with the H+-sensitive dye 2′,7′-bis-(carboxyethyl)-5-(and-6)-carboxyfluorescein were applied. The results show NBCe1 protein abundance and active NBCe1 transport. Hypoxia upregulated NBCe1 protein and activity in MES-like hypoxia-dependent GBM cells. This effect was positively correlated with HIF-1α protein levels, was mediated by TGF-β signalling, and was prevented by extracellular acidosis. In MES-like hypoxia-independent GBM cells, acidosis (but not hypoxia) regulated NBCe1 activity in an HIF-1α-independent manner. These results demonstrate a cell-specific adaptation of NBCe1 expression and activity to the microenvironment challenge of hypoxia and acidosis that depends on their transcriptional signature in GBM.
Prevalence of Intestinal Parasites Among female food handlers in Owerri Municipal, Imo State, Nigeria.

:Food handlers offer very important services to the general public especially those who are either on transit or those who could not afford to eat before leaving their homes and thus serve as veritable source of intestinal parasite transmission. A cross sectional survey on the prevalence of intestinal parasites among female food handlers in Owerri municipality was carried out between June and December, 2021. A total of 456 stool specimens were aseptically collected from food handlers and analysed using wet mounts and concentration techniques while a well-structured questionnaire was used to source information on demographic characteristics of the handlers. The result obtained revealed that out of 456 food handlers examined, 247(54.2%) had intestinal parasites. Among the six parasites (Ascaris lumbricoides, Hookworm, Trichuris trichuria, Strongyloides stercoralis, Enteamoeba histolytica and Giardia lamblia) encountered, Ascaris lumbricoides had the highest prevalence of 190(76.9%) while Giardia lamblia had the least prevalence of 5(2.0%). Age related prevalence showed age group 41 and above of having the highest prevalence of 38(66.7) while the least infected group was age <20 with prevalence of 41(41.8%). The distribution of the parasites with respect to age also showed age groups 21 -30 and 31 – 40 of having highest parasites distribution while the least group was age >20 with least parasites. Factors associated with the parasites transmissions showed significant differences, (P<0.05) on educational status, sources of water available to the handlers and preparation sites. However, prevalence does not depend on the selling points, (P>0.05). The results from the survey have revealed the dangers the handlers and those that patronize them are exposed to. Therefore, urgent education of the handlers on the benefits of personal hygiene and environmental sanitation should be encouraged. Government at all levels should return the services of public health officers to help curb the menace of improper handling of foods through regular inspection. Awareness on the importance of clean environment and danger of not complying with good hygiene should be carried out in the area.
Impact of the intensi ﬁ cation of beef production in Brazil on greenhouse gas emissions and land use

Brazilhasthelargestherdofbeefcattleintheworld,estimatedatapproximately200millionanimals.Production is predominantly pasture-based and low input and hence time to slaughter is long, which promotes high meth-ane(CH 4 ) emissions perkgof product. Theobjective of this study wasto investigate the impact ofincreasingan- imal productivity using fertilizers, forage legumes, supplements and concentrates, on the emissions of greenhouse gases (GHGs) in ﬁ ve scenarios for beef production in Brazil. A life cycle analysis (LCA) approach, from birth of calves to mature animals ready for slaughter at the farm gate, was utilized using Tier 2 methodologies of the IPCC and the results expressed in equivalents of carbon dioxide (CO 2 eq) per kg of carcass produced. FossilCO 2 emitted inthe production of supplements, feeds and fertilizers was includedusingstandard LCA tech- niques. The ﬁ rst four scenarios were based solely on cattle production on pasture, ranging from degraded Brachiaria pastures, through to a mixed legume/ Brachiaria pasture and improved N-fertilized pastures of Guinea grass ( Panicum maximum ). Scenario 5 was the most intensive and was also based on an N-fertilized Guinea grass pasture, but with a 75-day ﬁ nishing period in con ﬁ nement with total mixed ration (TMR). Across thescenariosfrom1to5theincreaseindigestibilitypromotedareductionintheforageintakeperunitofanimal weight gain and a concomitant reduction in CH 4 emissions. For the estimation of nitrous oxide (N 2 O) emissions from animal excreta, emission factors from a study in the Cerrado region were utilized which postulated lower emissionfromdungthanfromurineandmuchloweremissionsinthelongdryseasoninthisregion.Thegreatest impact of intensi ﬁ cation of the beef production systems was a 7-foldreduction of the areanecessary for production from 320 to 45 m 2 /kg carcass. Carcass production increased from 43 to 65 Mg per herd across the scenarios from 1 to 5, and total emissions per kg carcass were estimated to be reduced from 58.3 to 29.4 kg CO 2 eq/kg car-cass.Even thoughanimalweightgainwaslowerinthemixedgrass-legumescenario(3)thanfor theN-fertilized Guinea grass pastures (scenarios 4 and 5) GHG emissions per kg carcass were similar as the legume N 2 ﬁ xation input had no fossil-fuel cost. A large source of uncertainty for the construction of such LCAs was the lack of data for enteric CH 4 emissions from cattle grazing tropical forages.
Ethics on corporate Social Responsibility and organizational development in banking Industry of Nepal

Introduction: Banks are service oriented organization. They are the financial bodies which are related with the development of society. These institutions are the driving factor for Nepalese economic growth and development. Banks have integrated their activities for social development. So, they are the bodies for earning social fidelity. However, with these discrete roles, responsibilities also mount alongside. Banks are thus responsible for social upliftment. The process of leading development with inclusive social development is known as Corporate Social Responsibility (CSR). In today’s commercial world, corporate sectors like banks can only be successful through the coordinative efforts of social development. Hence, ethics and CSR plays vital role in maturing banking industry of Nepal. Objectives: In the mean-time of pandemic, banking industry has to perform swiftly through integrated workforce. This can only be possible by communal coordination and business to public (B2P) approach. This enhances the better outcome. So, the main objective of this research is to explore the needs of ethics on corporate social responsibility and organizational development in banking industry of Nepal. Design: Research regarding the banks is also crucial for understanding the economic norms and development of society. Thus, these researches have to be done carefully with in-depth study. So, for this research researcher has selected convenience sampling method. The researcher has designed self-structured questionnaire for collecting data from field. This research uses quantitative research design for analyzing research queries precisely. Likewise, research defines the results through statistical tools through SPSS software. Findings: Banks are the exemplary corporate institutions that reveal the ideas for other bodies to conduct the business. Corporate Social Responsibility (CSR) is asks of today’s corporate world and Nepalese banks has to admit those facts. CSR can help to build relationship among the society where organization performs their business. CSR links business with society thus is the crucial factors for enhancing success in service-oriented
Connected Tur\'an number of trees

As a variant of the much studied Tur´an number, ex( n, F ), the largest number of edges that an n -vertex F -free graph may contain, we introduce the connected Tur´an number ex c ( n, F ), the largest number of edges that an n -vertex connected F -free graph may contain. We focus on the case where the forbidden graph is a tree. The celebrated conjecture of Erd˝os and S´os states that for any tree T , we have ex( n, T ) ≤ ( | T |− 2) n 2 . We address the problem how much smaller ex c ( n, T ) can be, what is the smallest possible ratio of ex c ( n, T ) and ( | T | − 2) n 2 as | T | grows. We also determine the exact value of ex c ( n, T ) for small trees, in particular for all trees with at most six vertices. We introduce general constructions of connected T -free graphs based on graph parameters as longest path, matching number, branching number, etc.
Function Classes for Identifiable Nonlinear Independent Component Analysis

Unsupervised learning of latent variable models (LVMs) is widely used to represent data in machine learning. When such models reflect the ground truth factors and the mechanisms mapping them to observations, there is reason to expect that they allow generalization in downstream tasks. It is however well known that such identifiability guaranties are typically not achievable without putting constraints on the model class. This is notably the case for nonlinear Independent Component Analysis, in which the LVM maps statistically independent variables to observations via a deterministic nonlinear function. Several families of spurious solutions fitting perfectly the data, but that do not correspond to the ground truth factors can be constructed in generic settings. However, recent work suggests that constraining the function class of such models may promote identifiability. Specifically, function classes with constraints on their partial derivatives, gathered in the Jacobian matrix, have been proposed, such as orthogonal coordinate transformations (OCT), which impose orthogonality of the Jacobian columns. In the present work, we prove that a subclass of these transformations, conformal maps, is identifiable and provide novel theoretical results suggesting that OCTs have properties that prevent families of spurious solutions to spoil identifiability in a generic setting.
Monte Carlo neutron transport using low power mobile GPU devices

The using of GPU for Monte Carlo particle transport is lacking of fair comparisons. This work performs simulations on both CPU and GPU in the same package under the same manufacturing process of low power mobile devices. The experiment with simple pincell benchmark problems with fresh fuel gives consistent results between CPU and GPU. In the meanwhile, it finds that the Apple M1 GPU is as twice capable as M1 CPU, while entitled with a 5 times advantage in power consumption. The particle sorting algorithm optimized for GPU improves computing efficiency by 28\%, while prominently reducing GPU power consumption. Such advantage of sorting algorithm is expected to be greater for depleted fuel problems than fresh fuel problem. The kernel reconstruction Doppler broadening algorithm designed for continuously varying materials is demonstrated to produce consistent Doppler coefficients with the reference code and the algorithm can be efficiently implemented on GPU. Compared with the reference code with double precision floating point numbers, the testing codes with single precision floating point numbers could underestimate the K-effective values by about 500 pcm, and the Doppler coefficients of the fuel are well reproduced though. The conclusion may strengthen the argument that it is helpful for high performance computer to adopt GPU in order to reduce gross power consumption.
A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances’ switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.
Sparse Probability of Agreement

Measuring inter-annotator agreement is important for annotation tasks, but many metrics require a fully-annotated set of data, where all annotators annotate all samples. We define Sparse Probability of Agreement, SPA, which estimates the probability of agreement when not all annotator-item-pairs are available. We show that under certain conditions, SPA is an unbiased estimator, and we provide multiple weighing schemes for handling data with various degrees of annotation.
A Modular Framework for Reinforcement Learning Optimal Execution

In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups. Rather than focusing on agents and optimization methods, we focus on the environment and break down the necessary requirements to simulate an Optimal Trade Execution under a Reinforcement Learning framework such as data pre-processing, construction of observations, action processing, child order execution, simulation of benchmarks, reward calculations etc. We give examples of each component, explore the difficulties their individual implementations \&the interactions between them entail, and discuss the different phenomena that each component induces in the simulation, highlighting the divergences between the simulation and the behavior of a real market. We showcase our modular implementation through a setup that, following a Time-Weighted Average Price (TWAP) order submission schedule, allows the agent to exclusively place limit orders, simulates their execution via iterating over snapshots of the Limit Order Book (LOB), and calculates rewards as the \$ improvement over the price achieved by a TWAP benchmark algorithm following the same schedule. We also develop evaluation procedures that incorporate iterative re-training and evaluation of a given agent over intervals of a training horizon, mimicking how an agent may behave when being continuously retrained as new market data becomes available and emulating the monitoring practices that algorithm providers are bound to perform under current regulatory frameworks.
A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization

— Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model’s objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a practical SLF (PSLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that PSLF model has a competitive advantage over state-of-the-art models in data representation ability.
1 2 A ug 2 02 2 Direct Route to Thermodynamic Uncertainty Relations

Thermodynamic uncertainty relations (TURs) bound the dissipation in non-equilibrium systems from below by fluctuations of an observed current. Contrasting the elaborate techniques employed in existing proofs, we here prove TURs directly from the Langevin equation. This establishes the TUR as an inherent property of overdamped stochastic equations of motion. By including current-density correlations we, moreover, derive a new sharpened TUR for transient dynamics. Our arguably simplest and most direct proof allows us to systematically determine conditions under which the different TURs saturate and thus allows for a more accurate thermodynamic inference.