Miracle-dz/UniFAR-FTUs huggingface dataset:
Proteomic Clusters Underlie Heterogeneity in Preclinical AD Progression

Background: Heterogeneity in progression to AD poses challenges for both clinical prognosis and clinical trial implementation. In the absence of a well-defined understanding of future disease trajectory, participants may receive unnecessary treatment or true effects of pharmacological intervention may be obscured. We identified early differences in preclinical Alzheimer Disease (AD) biomarkers, assessed patterns for developing preclinical AD across the Amyloid-Tau-(Neurodegeneration) (AT(N)) framework, and considered potential sources of difference by analyzing the CSF proteome.Methods: 108 participants enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center (ADRC) who completed four or more lumbar punctures and were cognitively normal at baseline were included. Cerebrospinal fluid (CSF) measures of Aβ42, pTau181, and Neurofilament Light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including positron emission tomography (PET) amyloid and tau and structural magnetic resonance imaging (MRI) were repeatedly obtained when available. This allowed for staging individuals according to the AT(N) framework.Results and Conclusion: Growth mixture modeling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and CSF Aβ42. Two groups (AD Biomarker Positive and AD Biomarker Intermediate) had distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were successfully re-classified using only proteomic profiles (AUCAD Biomarker Positive vs AD Biomarker Negatives = 0.970, AUCAD Biomarker Positive vs. Intermediate AD Biomarkers = 0.750, AUCIntermediate AD Biomarkers vs. AD Biomarker Negative = 0.698). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified individuals who became AD Biomarker Positive before age 50. A second group, AD Biomarker Intermediate, developed elevated CSF ptau181 in their mid-60’s before becoming amyloid positive in their mid-70’s. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing vs. other agents) in clinical trials. CSF proteome analysis highlighted additional potential opportunities for non-AT(N) focused therapies, including blood brain barrier-, liver-, and neuroinflammatory-related targets.
SALSA: A Scheduling Algorithm for LoRa to LEO Satellites

Satellites are going to revolutionize the Internet of Things (IoT) connectivity enabling the ubiquitous coverage targeted by 5G and upcoming 6G networks. While the potentials of satellite IoT are unquestionable, many challenges still remain unsolved. In the present work, we design a Long Range Wide Area Network (LoRaWAN) with mobile gateways (GWs) installed on Low Earth Orbit (LEO) satellites. We address synchronization issues due to intermittent link availability between the End Devices (EDs) and the GW, and we propose a Scheduling Algorithm for LoRa to LEO Satellites (SALSA). SALSA ensures reliable communication, avoiding packets drops and packet collisions, by using a Time Division Multiple Access (TDMA) approach, rather than classic ALOHA-based LoRa. The uplink transmissions of the EDs are scheduled considering the satellite availability period, the satellite footprint, and the specific visit time for each ED. Two different policies are proposed: a First Come First Served (FCFS), and a FAIR policy. Simulation results, obtained in real conditions (with real satellites), demonstrate the better performance achievable with SALSA (regardless of the policy) compared to standard LoRa. The FAIR policy outperforms the FCFS policy by giving all the EDs an equal chance to transmit, even to those that are visited last by the satellite. The performance of SALSA can be further improved considering larger constellations of LEO satellites.
Mono Mode Vs Multiple Modes in the Context of Complexity Economics Through Mmtpi and Abtwram Both in Developing and Developed Countries

The patterns of evolutionary economics has illustrated the increasing gap of divergence between the traditional and assimilated realities, which were pointed out by Yin-Yang and vedic economics. The flow of research has illustrated the critical discussions on meso trajectory phases of innovation, and the interaction effects between Animal Spirits, Buchanan’s taxonomy, Wagner’s Law, Rahn-Armey Curve and Metcalfe’s Law in describing mono and multiple modes of complexity economics that are consist of actors of network, asymmetrical flow of information, inflationary risk, technological risk and environmental risk of public through the theory of structural change. The flow of research has proposed the balanced segments of government (proper regulation, the supervision of projects proposals and interactional upgrading of intellectual engagements), private and public institutions, actors of academic industry, nature of ethics, and etc that will be required for the maintenance of sustainable movement of evolutionary paradigm which will be directed through the concept on the modified version of meso trajectory phases of innovation that has also captured the main aspects of creative destruction (Mark 1), creative accumulation (Mark 2), mode of information accessibility, the number of actors in a structure of network. Besides that, the designed structure of this paper has dissected the importance of dual concepts of catching up and patching up from the ethnical view of the system.
Two Phase Flow Simulation of Fractal Oil Reservoir Based on Meshless Method

The reservoir is the networked rock skeleton of an oil and gas trap, as well as the generic term for the fluid contained within pore fractures and karst caves. Heterogeneity and a complex internal pore structure characterize the reservoir rock. By introducing the fractal permeability formula, this paper establishes a fractal mathematical model of oil-water two-phase flow in an oil reservoir with heterogeneity characteristics and numerically solves the mathematical model using the weighted least squares meshless method. Additionally, the method’s correctness is verified by comparison to the exact solution. The numerical results demonstrate that the fractal oil-water two-phase flow mathematical model developed using the meshless method is capable of more accurately and efficiently describing the flow characteristics of the oil-water two-phase migration process. In comparison to the conventional numerical model, this method achieves a greater degree of convergence and stability. This paper examines the effect of varying the initial viscosity of the oil, the initial formation pressure, and the production and injection ratios on daily oil production per well, water cut in the block, and accumulated oil in the block. For 10 and 60 cp initial crude oil viscosities, the water cut can be 0.62 and 0.80, with 3100 and 1900 m 3 cumulative oil production. Initial pressures have little effect on production. In this case, the daily oil production of well PRO1 is 1.7 m 3 at 7 and 10 MPa initial pressure. Block cumulative oil production is 3465.4 and 2149.9 m 3 when the production injection ratio is 1.4 and 0.8. The two-phase meshless method described in this paper is essential for a rational and effective study of production dynamics patterns in complex reservoirs and the development of reservoir simulations of oil-water flow in heterogeneous reservoirs.
Hepatitis B Virus Integration into Transcriptionally Active Loci and HBV-Associated Hepatocellular Carcinoma

Hepatitis B Virus (HBV) DNA integrations into the human genome are considered major causative factors to HBV-associated hepatocellular carcinoma development. In the present study, we investigated whether HBV preferentially integrates parts of its genome in specific genes and evaluated the contribution of the integrations in HCC development per gene. We applied dedicated in-house developed pipelines on all of the available HBV DNA integration data and performed a statistical analysis to identify genes that could be characterized as hotspots of integrations, along with the evaluation of their association with HBV-HCC. Our results suggest that 15 genes are recurrently affected by HBV integrations and they are significantly associated with HBV-HCC. Further studies that focus on HBV integrations disrupting these genes are mandatory in order to understand the role of HBV integrations in clonal advantage gain and oncogenesis promotion, as well as to determine whether inhibition of the HBV-disrupted genes can provide a therapy strategy for HBV-HCC.
Reading Poetry and Prose: Eye Movements and Acoustic Evidence

ABSTRACT We examined genre-specific reading strategies for literary texts and hypothesized that text categorization (literary prose vs. poetry) modulates both how readers gather information from a text (eye movements) and how they realize its phonetic surface form (speech production). We recorded eye movements and speech while college students (N = 32) orally read identical texts that we categorized and formatted as either literary prose or poetry. We further varied the text position of critical regions (text-initial vs. text-medial) to compare how identical information is read and articulated with and without context; this allowed us to assess whether genre-specific reading strategies make differential use of identical context information. We observed genre-dependent differences in reading and speaking tempo that reflected several aspects of reading and articulation. Analyses of regions of interests revealed that word-skipping increased particularly while readers progressed through the texts in the prose condition; speech rhythm was more pronounced in the poetry condition irrespective of the text position. Our results characterize strategic poetry and prose reading, indicate that adjustments of reading behavior partly reflect differences in phonetic surface form, and shed light onto the dynamics of genre-specific literary reading. They generally support a theory of literary comprehension that assumes distinct literary processing modes and incorporates text categorization as an initial processing step.
Elemental and macromolecular composition of the marine Chloropicophyceae, a major group of oceanic photosynthetic picoeukaryotes

Chloropicophyceae (Prasinophyte Clade VII) are small nonmotile coccoid cells with cell diameters ranging from 1 to 3 μm. Molecular surveys indicate they are relatively high in abundance in moderately oligotrophic oceanic waters and may substantively contribute to biogeochemical cycling in the sea. Here, we quantify the elemental and macromolecular composition of three subtropical Chloropicophyceae strains: Chloropicon mariensis, Chloropicon maureeniae, and Chloropicon roscoffensis under nutrient‐sufficient exponential growth and nitrate starvation. Under nutrient‐sufficient conditions the Chloropicophyceae are high in C : N and quite low in C : P and N : P relative to the canonical Redfield ratio, reflecting their relatively high nucleic acid composition compared to many other phytoplankton taxa. Nitrate starvation causes increases in C : N and C : P and decreases in N : P, primarily due to increases in carbohydrate and lipid and decreases in protein and RNA. There is genetic evidence that unlike most other green algae, Chloropicophyceae are diploid. The high nucleic acid content in the Chloropicon is consistent with the hypothesis that the nucleus, as a nonscalable component, takes up a larger and substantial proportion of cell mass in diploid picoeukaryotes. The elemental and macromolecular composition of these Chloropicophyceae, and relatively homeostatic response to N‐starvation compared to diatoms, provides some insight into their success in the moderately oligotrophic ocean.
How Do Irrigation and Drainage Interventions Secure Food Production and Livelihood for Rural Communities?

Irrigated agriculture is again at the forefront of development priorities, as food security and good nutrition are now considered among major goals, along with ending poverty and achieving access to water and sanitation for all. Question 59 of the 22nd ICID Congress, by addressing the two issues of ensuring food security and securing livelihoods in rural areas, is therefore particularly relevant within the Sustainable Development Goals (SDGs) process led by the United Nations.
Solvothermal Synthesis, Photocatalytic and Electrochemical Properties of Flower-Like TiO2 with Hierarchical Structures

Flower-like TiO2 hierarchical structures were synthesized by a solvothermal strategy using tetrabutyl titanate as the titanium source. The obtained flower-like TiO2 hierarchical structures were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), specific surface area analysis, Raman spectroscopy and energy-dispersive spectroscopy (EDS). Results show that the flower-like hierarchical structures are composed of anatase titanium dioxide. TiO2 nanowires are first formed by the self-assembly of nanocrystals with a diameter of 20 nm, and then several TiO2 nanowires produce a petal-like structure. Finally, flower-like TiO2 hierarchical structures with a diameter of 2 ~ 2.3 μm are assembled by these petal-like structures. The electrochemical properties of the flower-like TiO2 hierarchical structures were studied by using these hierarchical structures as the anode material in a lithium-ion battery. The flower-like TiO2 hierarchical structures have an initial discharge capacity of 473.9 mAh/g at a current density of 100 mA/g and an initial discharge capacity of 244.4 mAh/g at a current density of 2 A/g in the potential range of 0.01~3 V. The photocatalytic properties of the flower-like TiO2 hierarchical structures were studied by degrading methyl orange (MO) solution under ultraviolet (UV) light irradiation. When used as a photocatalyst, the degradation rate of MO is 95.8% after 40 min of UV irradiation, showing that the flower-like TiO2 hierarchical structures have excellent photocatalytic activity.
Patent Citations: An Examination of the Data Generating Process

Existing measures of innovation often rely on patent citations to indicate intellectual lineage and impact. We show that the data generating process for patent citations has changed substantially since citation-based measures were validated a decade ago. Today, far more citations are created per patent, and the mean technological similarity between citing and cited patents has fallen significantly. These changes suggest that the use of patent citations for scholarship needs to be re-validated. We develop a novel vector space model to examine the information content of patent citations, and show that methods for sub-setting and/or weighting informative citations can substantially improve the predictive power of patent citation measures. We make data for a basic correction available for future scholarship through the Patent Research Foundation.
Single cell profiling reveals functional heterogeneity and serial killing in human peripheral and ex vivo-generated CD34+ progenitor derived Natural Killer cells

Increasing evidence suggest that Natural killer (NK) cells are composed of distinct functional subsets. This multi-functional role displayed by NK cells have made them an attractive choice for anti-cancer immunotherapy. A functional NK cell repertoire is generated through cellular education, resulting in heterogeneous NK cell population with distinct capabilities to respond to different stimuli. The application of a high-throughput droplet-based microfluidic platform allows monitoring of NK cell-target cell interactions at single-cell level and in real-time. Through fluorescence-based screening of around 80,000 droplets, with different Effector:Target ratios, a fully automated image analysis allows for the assessment of individual killing events in each droplet over time. We observed a variable response of single NK cells towards different target cells and identified a distinct population of NK cells capable of inducing multiple target lysis, coined as serial killers. To meet the increasing clinical demand for NK cells several sources, such as umbilical cord blood (UCB), have successfully been explored. By assessing the cytotoxic dynamics, we showed that single UCB-derived CD34+ hematopoietic progenitor (HPC)-NK cells display superior anti-tumor cytotoxicity. Additionally, with an integrated analysis of cytotoxicity and cytokine secretion we showed that target cell interactions augmented cytotoxic as well as secretory behavior of NK cells. By providing an in-depth assessment over NK cell functions, this study provides crucial information on diversity and functional characteristics of peripheral blood NK cells and ex vivo-generated HPC-NK cells to develop and improve of NK cell-based cancer immunotherapy.
Linear Quadratic Control Using Model-Free Reinforcement Learning

In this article, we consider linear quadratic (LQ) control problem with process and measurement noises. We analyze the LQ problem in terms of the average cost and the structure of the value function. We assume that the dynamics of the linear system is unknown and only noisy measurements of the state variable are available. Using noisy measurements of the state variable, we propose two model-free iterative algorithms to solve the LQ problem. The proposed algorithms are variants of policy iteration routine where the policy is greedy with respect to the average of all previous iterations. We rigorously analyze the properties of the proposed algorithms, including stability of the generated controllers and convergence. We analyze the effect of measurement noise on the performance of the proposed algorithms, the classical off-policy, and the classical <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-learning routines. We also investigate a model-building approach, inspired by adaptive control, where a model of the dynamical system is estimated and the optimal control problem is solved assuming that the estimated model is the true model. We use a benchmark to evaluate and compare our proposed algorithms with the classical off-policy, the classical <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-learning, and the policy gradient. We show that our model-building approach performs nearly identical to the analytical solution and our proposed policy iteration-based algorithms outperform the classical off-policy and the classical <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-learning algorithms on this benchmark but do not outperform the model-building approach.
MEA-Pensim 2.0: Weiterentwicklung eines Rentensimulationsmodells, Konzeption und ausgewählte Anwendungen (MEA-Pensim 2.0: Advancement of a Pension Simulation Model, Conception and Selected Applications)

German Abstract: Die demographische Entwicklung stellt die Gesetzliche Rentenversicherung vor enorme Her-ausforderungen. Vor allem steht die Frage nach der zukunftigen Finanzierbarkeit des Sys-tems in seiner heutigen Form im Mittelpunkt der Diskussion. Um die zukunftige Entwicklung sowie die Auswirkungen verschiedener Faktoren und moglicher Reformen auf das deutsche Rentensystem prognostizieren zu konnen, wird ein Simulationsmodell benotigt, welches das gesetzliche Rentensystem inklusive aller entscheidenden Determinanten (u.a. Bevolkerung, Arbeitsmarkt) abbildet. Ziel dieses Papiers ist es, das Rentensimulationsmodell MEA-Pensim 2.0 vorzustellen. Dabei handelt es sich um eine Weiterentwicklung des Rentensimulations-modells MEA-Pensim aus dem Jahr 2004. Das Papier gliedert sich hierbei in sechs Abschnitte, wobei das Modell in den Abschnitten 2, 3 und 4 beschrieben wird. Im funften Abschnitt werden ausgewahlte Anwendungen vorgestellt und diskutiert. So werden u.a. unterschiedliche Bevolkerungs- und Arbeitsmarktentwicklungen sowie der Vorschlag der Bundesregierung zur Anhebung der Zurechnungszeit auf ihre Auswirkungen untersucht. Abschliesend wird ein Ausblick auf offene Fragestellungen gegeben.English Abstract: The German Pension System is confronted with huge challenges by the demographic trend. Especially the current systems financially viability is in focus of the present discussion. To be able to prognosticate the future development and impacts of different factors or possible reforms on the German Pension System, we need a simulation model, which reproduces the pension system and all its critical determinants (i.e. the population and the labour market). The goal of this paper is to present the pension simulation model MEA‐Pensim 2.0, an advancement of the pension simulation model MEA‐Pensim of the year 2004. Therefore, this paper is divided into six chapters. The model is described in the chapters 2, 3 and 4. In the fifth chapter we present and discuss some selected applications. In this context we analyse, among other things, different population and labour market developments as well as the impacts of the proposal by the federal government to extend the supplementary period. Finally we give a perspective on open questions that remain.
Lean requirements traceability automation enabled by model-driven engineering

Background The benefits of requirements traceability, such as improvements in software product and process quality, early testing, and software maintenance, are widely described in the literature. Requirements traceability is a critical, widely accepted practice. However, very often it is not applied for fear of the additional costs associated with manual efforts or the use of additional tools. Methods This article presents a “low-cost” mechanism for automating requirements traceability based on the model-driven paradigm and formalized by a metamodel for the creation and monitoring of traces and an integration process for traceability management. This approach can also be useful for information fusion in industry insofar that it facilitates data traceability. Results This article extends an existing model-driven development methodology to incorporate traceability as part of its development tool. The tool has been used successfully by several companies in real software development projects, helping developers to manage ongoing changes in functional requirements. One of those projects is cited as an example in the paper. The authors’ current work leads them to conclude that a model-driven engineering approach, traditionally used only for the automatic generation of code in a software development process, can also be used to successfully automate and integrate traceability management without additional costs. The systematic evaluation of traceability management in industrial projects constitutes a promising area for future work.