Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Assembly of Protein Fibrils in Microgravity

Abstract Deposits of insoluble protein fibrils in human tissue are associated with amyloidosis and neurodegenerative diseases. Different proteins are involved in each disease; all are soluble in their native conformation in vivo, but by molecular self-assembly, they all form insoluble protein fibril deposits with a similar cross β-sheet structure. This paper reports the results of an experiment in molecular self-assembly carried out in microgravity on the International Space Station (ISS). The Self-Assembly in Biology and the Origin of Life (SABOL) experiment was designed to study the growth of lysozyme fibrils in microgravity. Lysozyme is a model protein that has been shown to replicate the aggregation processes of other amyloid proteins. Here the design and performance of the experimental hardware is described in detail. The flight experiment was carried to the ISS in the Dragon capsule of the SpaceX CRS-5 mission and returned to Earth after 32 days. The lysozyme fibrils formed in microgravity aboard the ISS show a distinctly different morphology compared to fibrils formed in the ground-control (G-C) experiment. The fibrils formed in microgravity are shorter, straighter, and thicker than those formed in the laboratory G-C experiment. For two incubation periods, (2) about 8.5 days and (3) about 14.5 days, the average ISS and G-C fibril diameters are respectively: Period 2DISS=7.5nm±31%,andDG‐C=3.4nm±31%Period 3DISS=6.2nm±33%,andDG‐C=3.6nm±33%. \matrix{{Period\,2} \hfill & {} \hfill & {{D_{ISS}} = 7.5{\rm{nm}} \pm 31\% ,} \hfill \cr {} \hfill & {\rm and} \hfill & {{D_{G - C}} = 3.4{\rm{nm}} \pm 31\%} \hfill \cr {Period\,3} \hfill & {} \hfill & {{D_{ISS}} = 6.2{\rm{nm}} \pm 33\% ,} \hfill \cr {} \hfill & {\rm and} \hfill & {{D_{G - C}} = 3.6{\rm{nm}} \pm 33\% . }}
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks

Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initialization, insufficient overlap or in the presence of dynamic objects. Modern deep learning based registration approaches present much better results, but suffer from a heavy runtime. We overcome these drawbacks by introducing StickyPillars, a fast, accurate and extremely robust deep middle-end 3D feature matching method on point clouds. It uses graph neural networks and performs context aggregation on sparse 3D key-points with the aid of transformer based multi-head self and cross-attention. The network output is used as the cost for an optimal transport problem whose solution yields the final matching probabilities. The system does not rely on hand crafted feature descriptors or heuristic matching strategies. We present state-of-art art accuracy results on the registration problem demonstrated on the KITTI dataset while being four times faster then leading deep methods. Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset. Finally, we demonstrate robustness on KITTI odometry. Our method remains stable in accuracy where state-of-the-art procedures fail on frame drops and higher speeds.
Scientific Aptitude and Academic Achievement of Students in Mathematics

Scientific Aptitude Test (SAT) is intended to measure the potential ability of a person to pursue as course of training in the field of Science related subjects. Academic Achievement in Mathematics helps to inculcate the skills of quantification of experiences around the learners. The concept of achievement involves the interaction of three factors like aptitude, readiness and opportunity of learning. The objective of the study was to determine the significant difference in the level of Scientific Aptitude with respect to variables like gender and locale. The Descriptive method of Normative Study type was adopted. A sample of 100 students was selected for the study from the Senior Secondary schools of West Bengal. Tool Used for the Study are Scientific Aptitude Test (SAT) tool developed by Shahapur and Rao (2006). Findings of the Study were that Boys possess more Scientific Aptitude than Girls and Scientific Aptitudes of Urban students are more than that of rural students.
Quantifying estuarine‐scale invertebrate larval connectivity: Methodological and ecological insights

The early life history of many marine organisms includes a dispersive planktonic larval phase which allows for the exchange of individuals among subpopulations. Knowledge of the degree of exchange, or connectivity, is critical to understanding the abundance and distribution of marine populations. Here, we applied geochemical tagging to assess estuarine‐scale larval connectivity among subpopulations of the commercially and ecologically important eastern oyster, Crassostrea virginica. To generate an “atlas” of geochemical signatures associated with spawning sites and potential dispersal pathways from spawning sites, we outplanted recently spawned oyster larvae to stationary moorings and surface drifters, respectively. Using the atlases generated from both outplant methods, we predicted natal origin, and thus larval connectivity, for newly settled oysters (spat) during three field trials over two summers (June 2013, June 2014, and August 2014), within three regions (∼ 35 km × 15 km quadrants) of Pamlico Sound, North Carolina, U.S.A. Patterns of larval connectivity varied both between months and annually but were predominately directed south to north following wind patterns. Predicted self‐recruitment was variable, as up to 100% of spat in a given region displayed signatures consistent with natal origin within that same region. Predicted connectivity patterns varied significantly based on atlases generated from outplanting on stationary moorings vs. surface drifters. For example, drifter‐predicted connectivity followed biophysical larval dispersal models more closely than mooring‐predicted connectivity, while mooring‐predicted connectivity displayed a higher diversity in larval sources. Both connectivity models highlight the need for resource management strategies such as reserve networks to incorporate designs that account for inherent variability in dispersal pathways.
Foliar Application of Cu Nanoparticles Modified the Content of Bioactive Compounds in Moringa oleifera Lam

Moringa oleifera Lam is a plant that has recently gained importance as a food because of its nutritional value and bioactive compound content and because practically all the organs are usable. The use of nanoparticles has appeared as an alternative to increase bioactive compounds in plants. The goal of this work was to determine if the application of copper nanoparticles would increase the content of bioactive compounds and antioxidant capacity in M. oleifera. Copper (Cu) nanoparticles were applied to the leaves at four different times throughout crop growth. The biocompounds were analyzed after the second, third, and fourth applications. The results show that application of Cu nanoparticles has a beneficial effect on the accumulation of bioactive compounds in M. oleifera leaves. In addition, the antioxidant capacity and carotenoid and chlorophyll contents in the leaves of M. oleifera increased after Cu nanoparticles application. The same effect was not observed in the fruit of M. oleifera. Here, the bioactive compound contents diminished. Therefore, the use of Cu nanoparticles can be an important alternative to improve the quality of this plant, particularly that of the leaves.
Graph Convolutional Networks for context-aware recommender systems

With more multimedia content being available than ever, the need of recommender systems is becoming crucial. Recent studies show that including context (such as daytime, last clicked items, weather, etc.) helps in order to give better recommendations. Factorization machines (FM) models [17] are an eﬀective solution for context-aware recommendation tasks. However, FM models are based on the second-order interactions between features, which cause one of its major drawbacks: they cannot capture complex high-order signal interactions. Some work has already been proposed ([8], [9] and [24]) in order to address this problem. Yet, these methods use deep neural networks (DNN) to learn high-order interactions between features, which makes them either too expressive ([8] and [9]) or too computational expensive ([24]). In this work, we propose to capture high-order interactions from the embedding level. We want the feature embedding to encode not only the information from the feature itself but also to aggregate the correlated knowledge from other features. Our approach to do so is by using Graph Convolutional Networks (GCN) instead of the common embedding layer that conforms recommendation models (such as FM). In that way, we will be able to keep the structure of FM models while having the high-order signals automatically contained in the interaction between feature embeddings. Our solution is implemented in Pytorch [11] and it will result in a module, which could be included in any recommender system thus leading to an end-to-end model. We build the work following the next
Acoustic fish communities: sound diversity of rocky habitats reflects fish species diversity

Assessing fish biodiversity patterns is a major concern in aquatic science and conservation. To be effectively used, fish diversity assessments benefit from the use of integrated complementary approaches. Passive acoustics has received increasing attention as a non-invasive, longterm monitoring tool, as it uses biological sounds produced incidentally or intentionally as natural tags to identify and estimate animal diversity. In the marine environment, there is little evidence about the link between taxonomic diversity (different species) and acoustic diversity (different sound types). Here we used underwater visual census fish data collected over multiple years from 3 sites within a Mediterranean Marine Protected Area as comprehensive information on local fish assemblages to be compared with acoustic recordings obtained in September 2015. Richness, diversity and community similarity indices as well as abundance analyses revealed a strong relationship between taxonomic diversity and acoustic diversity. Overall, acoustic communities showed pronounced differences between the study sites that were not observed in the respective taxonomic assemblages. Despite the lower number of sound type categories (12) compared to taxa (53) and the short recording period, passive acoustics showed a high discriminating potential, which supports its suitability as a complementary approach to visual-based surveys. The fish sound repertoire established here was organized into a dichotomous tree based on acoustic characteristics that are valuable for the development of automatic acoustic biodiversity appraisal tools for resource monitoring and management.
Challenging the Current Shariah Screening Methodology Assessments in Kuala Lumpur Shariah Index (KLSI)

The purpose of this study is to explain the justification behind the current Shariah screening methodology assessments used by KLSI, which differs from other major Shariah screening methodology around the world. Besides, this study is also attempts to investigate on the intention to entitle Shariah-compliant status by Public Listed Companies (PLCs) in Malaysia. This study employed a series of interviews with five individuals who are involves with KLSI Shariah screening methodology as data collection procedure. Thus, this study found a number of justifications on the reasons behind the current Shariah screening methodology assessments used by KLSI. This includes reasons on the adoption of additional 20% tolerable percentage, additional criteria on corporate image and maslaha (public benefits) and also views on the leniency of current KLSI Shariah screening methodology assessments. Besides, this study also identified several measures to identify the intention to entitle Shariah-compliant status by PLCs. This includes voluntary application, effort of de-listed PLCs to be listed as Shariah-compliant for the following year, impact of Shariah-compliant status especially on the share prices and potential investors, good corporate image on maintaining Shariah-compliant status and the core value of the business. Then, this study also provide several recommendations to be considered for future improvement especially on Shariah screening methodology assessment.
Neurohormonal Control of Lactation and Milk Let-down in Dairy Animals

Lactogenesis is the process of differentiation where by the mammary alveolar cell acquires ability to secrete the milk. Once initiated, secretion of milk by alveolar epithelial cells is a continuous process but secretion does not occur at a constant rate over time. Maintenance of secretion throughout an individual lactation cycle requires regular removal of milk from the gland (Alexandrova et al., 1980). Rate of milk secretion depends on available storage capacity within the gland. Rate of milk secretion is fastest immediately after milk removal via suckling or milking and slows dramatically by 10–12 hours. Implementing a shorter interval between subsequent bouts of milk removal can increase milk production capacity. Approximately 35 hours after the most recent milk removal, milk secretion essentially stops. Control of milk secretion is achieved through both physical and chemical interactions (Bisset et al., 1970).
Integrity and stability of virulence plasmids from Pseudomonas syringae are modulated by mobile genetic elements and multiple toxin-antitoxin systems

Background Virulence plasmids are critically exposed to genetic decay and loss, particularly in Pseudomonas syringae strains because of their high content of mobile genetic elements and their exploitation of environmental niches outside of the plant host. The demonstrated high plasticity and adaptability of P. syringae plasmids, involving the acquisition and loss of large DNA regions, contrasts with their usual high stability and the maintenance of key virulence genes in free living conditions. The identification of plasmid stability determinants and mechanisms will help to understand their evolution and adaptability to agroecosystems as well as to develop more efficient control measures. Results We show that the three virulence plasmids of P. syringae pv. savastanoi NCPPB 3335 contain diverse functional stability determinants, including three toxin-antitoxin systems (TA) in both pPsv48A and pPsv48C, whereas one of the two replicons of pPsv48C can confer stable inheritance by itself. Loss of pPsv48A increased by two orders of magnitude upon functional inactivation of its TA systems. However, inactivation of the TA systems from pPsv48C did not result in its curing but led to the recovery of diverse deletion derivatives. One type consisted in the deletion of an 8.3 kb fragment, with a frequency of 3.8 ± 0.3 × 10−3, by recombination between two copies of MITEPsy2. Likewise, IS801 promoted the occurrence of deletions of variable size by one-ended transposition with a frequency of 5.5 ± 2.1 × 10−4, 80 % of which resulted in the loss of virulence gene idi. These deletion derivatives were stably maintained in the population by replication mediated by repJ, which is adjacent to IS801. IS801 also promoted deletions in plasmid pPsv48A, either by recombination or one-ended transposition. In all cases, functional TA systems contributed significantly to reduce the occurrence of plasmid deletions in vivo. Conclusions Virulence plasmids from P. syringae harbour a diverse array of stability determinants with a variable contribution to plasmid persistence. Additionally, multiple TA systems favour the long-term survival and integrity of virulence plasmids, as well as the maintenance of pathogenicity genes in free-living conditions. This strategy is likely widespread amongst native plasmids of P. syringae and other bacteria.
Multiscale dynamic mean field (MDMF) model to relate resting state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis in health and disease

The spontaneous neuronal activity of human brain shows highly structured spatio-temporal pattern, designated as resting state network. Previous neuro-computational studies have established the connection of spontaneous resting state activity with neuronal ensembles (using dynamic mean field approach) and showed the impact of local inhibitory-excitatory balance on this patterned spontaneous resting state activity of brain. But, the role of neurotransmitter dynamics on large-scale network organization remains unexplored. Empirically, this is beyond the scope of current neuroimaging techniques to monitor the neurotransmitter changes and capture BOLD dynamics simultaneously. We argue that large-scale computational models offer an attractive platform to test out the possible neural mechanisms that operate at multiple scales of physiological organization namely, brain metabolism and changes in synaptic concentrations of GABA (gamma-aminobutyric acid)/ glutamate. We introduce a multiscale dynamic mean field (MDMF) model, that captures the synaptic gating dynamics as a function of neurotransmitter kinetics, we demonstrate how local homeostasis of excitatory-inhibitory neurotransmitter concentrations (GABA/ glutamate) could modulate the large-scale resting state network dynamics of brain. Simulations were carried out across glutamate (0.1 to 15 mmol) and GABA (0.1 to 15 mmol) concentration regimes that span the parameter space reported from healthy and diseased brains. We have found for optimal configurations of glutamate concentration ranging from 5.6 to 15 mmol and GABA concentration from 0.9 to 15 mmol model predicted functional connectivity matrix show closest match with empirical functional connectivity matrix of normal resting brain. Graph theoretical measures of segregation (modularity, clustering coefficient and local efficiency) and integration (global efficiency and characteristic path length) in network information processing reported earlier in healthy and diseased brains were related to optimal GABA and glutamate concentrations and pathological values reported in epilepsy and schizophrenia respectively. In conclusion, the MDMF model could relate the various scales of observations from neurotransmitter concentrations to dynamics of synaptic gating to large-scale resting state network topology in healthy and in disordered states of the brain.
Analisis Pembentukan Portofolio Optimal Saham dengan Menggunakan Model Indeks Tunggal (Studi Kasus pada Perusahaan Terdaftar ii Indeks Sri-Kehati Periode 2016-2018)

Portofolio saham merupakan investasi yang terdiri dari berbagai saham perusahaan dengan harapan bila harga salah satu saham menurun, sementara yang lain meningkat maka investasi tersebut tidak mengalami kerugian. Penelitian ini bertujuan untuk mengetahui saham yang terdaftar dalam indeks SRI-KEHATI yang memenuhi kriteria pembentukan portofolio optimal. Analisis yang digunakan dalam pembentukan portofolio optimal pada penelitian ini menggunakan penerapan Model Indeks Tunggal. Sampel penelitian ini adalah saham perusahaan pada Indeks SRI KEHATI dan secara konsisten terdaftar dalam indeks SRI-KEHATI periode 2016-2018. Data yang digunakan adalah data sekunder. Hasil penelitian menunjukkan bahwa dari 22 saham sampel penelitian, terdapat 7 saham yang merupakan saham pembentuk portofolio optimal yaitu JPFA, TINS, BBCA, UNTR, BDMN, BMRI, BBNI dengan proporsi dana masing-masing sebesar 0.1359 atau 13,59% untuk JPFA, 0.0439 atau 4,39% untuk TINS, 0,5950 atau 59,50% untuk BBCA, 0.0751 atau 7,51% untuk UNTR, 0.0875 atau 8,75% untuk BDMN, 0.0427 atau 4,27% untuk BBRI, 0,0199 atau 1,99% untuk BBNI. Portofolio yang terbentuk dari ketujuh saham tersebut mampu memberikan expected return sebesar 2,625% dan memiliki risiko sebesar 0,15%.
Novel role of the central unstructured Paramyxovirus nucleopcapsid protein tail domain

The paramyxovirus replication machinery comprises the viral large (L) and phospho-(P) proteins in addition to the nucleocapsid (N) protein that encapsidates the single stranded RNA genome. Common to paramyxovirus N proteins is a C-terminal tail (Ntail), which contributes to docking of the polymerase complex to the genome through defined interaction domains. The central Ntail section is structurally disordered and thought to provide spatial flexibility required for productive interaction of the polymerase with the encapsidated viral genome, but its mechanistic role and relevance for successful virus replication is untested. Focusing initially on members of the morbillivirus genus, a series of Measles virus (MeV) and Canine distemper virus (CDV) N proteins were generated with internal deletions in the unstructured tail section. N proteins with large tail truncations remained bioactive in monoand polycistronic minireplicon assays and supported efficient replication of recombinant viruses. Bioactivity of Ntail mutants extended to N proteins derived from highly pathogenic Nipah virus (NiV), a member of the henipavirus genus. To probe an effect of Ntail truncations on viral pathogenesis, recombinant CDVs were analyzed in a lethal CDV/ferret model of morbillivirus disease. The recombinant viruses displayed different stages of attenuation ranging from ameliorated clinical symptoms to complete survival of infected animals, depending on the molecular nature of the Ntail truncation. Reinfection of surviving animals with pathogenic CDV revealed robust protection against a lethal challenge. The highly attenuated was genetically stable after extensive ex vivo passaging and recovery from infected animals. Mechanistically, gradual viral attenuation coincided with stepwise-altered viral transcriptase activity in infected cells. These results identify the central Ntail section as a determinant for viral pathogenesis and establish a novel platform to engineer gradual virus attenuation for next-generation paramyxovirus vaccine design. INDEX WORDS: Paramyxoviruses, Nucleoprotein, Nucleocapsid, MeV, CDV NOVEL ROLE OF THE CENTRAL UNSTRUCTURED PARAMYXOVIRUS NUCLEOCAPSID PROTEIN TAIL DOMAIN
Dynamic modelling of hepatitis C transmission among people who inject drugs: A tool to support WHO elimination targets

To reach the WHO goal of hepatitis C elimination, it is essential to identify the number of people unaware of their hepatitis C virus (HCV) infection and to investigate the effect of interventions on the disease transmission dynamics. In many middle- and high-income countries, one of the primary routes of HCV transmission is via contaminated needles shared by people who inject drugs (PWIDs). However, substantial underreporting combined with high uncertainty regarding the size of this difficult to reach population, makes it challenging to estimate the core indicators recommended by the WHO. To help enable countries to monitor their progress towards the elimination goal, we present a novel multi-layered dynamic transmission model for HCV transmission within a PWID population. The model explicitly accounts for disease stage (acute and chronic), injection drug use status (active and former PWIDs), status of diagnosis (diagnosed and undiagnosed) and country of disease acquisition (domestic or abroad). First, based on this model, and using routine surveillance data, we estimate the number of undiagnosed PWIDs, the true incidence, the average time until diagnosis, the reproduction numbers and associated uncertainties. Second, we examine the impact of two interventions on disease dynamics: 1) direct-acting antiviral drug treatment, and 2) needle exchange programs. To make the model accessible to relevant users and to support communication of our results to public health decision makers, the model and its output are made available through a Shiny app. As a proof of concept, we illustrate our results for a specific data set; however, through the app our model can be easily adapted to other high-income countries with similar transmission patterns among PWIDs where the disease is endemic.