Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Evaluation of Patties Prepared from Pacific White Shrimps (Litopenaeus vannamei) Grown in Inland Saline Water and Brackish Water Regimes during Frozen Storage

ABSTRACT In this study, an attempt has been made to develop shrimp patties from inland saline water reared vannamei (ISRV) and compare its quality with that from brackish water reared vannamei (BWRV) under storage conditions at −18°C for 4 months. The patties prepared from ISRV had tantamount protein (21.69%), high ash (1.62%) and low fat (0.31%) content as compared with BWRV patties, which had protein, ash and fat content as 22.45%, 1.52% and 0.43% respectively. No significant difference was observed in sensory scores of the patties. Trimethylamine, total volatile base nitrogen, total viable bacterial count and staphylococcal count were significantly increasing during storage and are within the acceptable limits. Peroxide value and TBARS increased during storage and slightly higher values observed in ISRV. This study concluded that overall quality and shelf life of patties prepared from ISRV was comparable with BWRV and there is no significant effect observed under frozen conditions.
“We Need to Feel Safe”: Experiences of Gifted LGBTQ+ Students and Implications for School Counselors

Abstract Individuals who identify as gifted or LGBTQ + often have qualitatively different academic, social, and emotional experiences throughout their adolescent development. However, scant research exists exploring the development of individuals who hold these intersecting identities. Using an anonymous retrospective qualitative survey, 75 participants who identify as both gifted and LGBTQ + shared narratives about their development as gifted LGBTQ + students. Using interpretive phenomenological analysis, the data revealed three superordinate themes that articulated the overall description of the gifted LGBTQ + participants’ development. Implications of the results for school counselors are provided.
Comparative Analysis of the Effect of Ultrasound-Assisted and Conventional Water Bath Extraction Methods on the Physicochemical Characteristics of Tilapia Scales Gelatin

ABSTRACT This study explored the effect of ultrasound (US)-assisted, and water bath (WB) extractions (at 60°C for 3 hours) on the physicochemical properties of gelatin from the scales of Oreochromis niloticus. US resulted in higher protein (76.19%), ash (0.98%) contents and yield (14.32%) compared to WB (67.51%, 0.35%, and 12.15%). But there was no significant effect on gel strength and melting point by the extraction method. US resulted in less viscous gel with a lower setting point, but with a better transmittance than that of WB. FTIR spectra showed the presence of amide regions in all gelatins. Extracted gelatins showed higher triple-helix contents than the commercial gelatin as revealed by X-ray spectra, while US reduced it than WB. Therefore, the tilapia scales were suitable to extract gelatin and ultrasound-assisted extraction was a promising way to increase its yield while achieving better gel characteristics.
Organic carbon content in the diagnostic horizons and materials of South African soil forms

Soil classification aims to group similar soils to facilitate communication and management. Management of soil organic carbon is gaining international recognition in an attempt to manage atmospheric carbon in addressing global warming. This paper explored the soil morphological and analytical data from a land type survey of South Africa, as augmented by data from other soil surveys in the country, to establish if soil classification, especially the soil form, can indicate the expected soil organic carbon content and thus enable generalisations to be drawn. Average organic carbon in the diagnostic topsoil horizons followed a predictable pattern: organic O (9.51% OC) > humic A (3.69% OC) > melanic A (2.59% OC) > vertic A (1.49% OC) > orthic A (1.38% OC). However, the organic carbon content also differed within the same diagnostic horizon between different soil forms. These differences could largely be related to the nature of the overlying topsoil, where topsoils with higher organic carbon contents typically resulted in subsoils with higher organic carbon. The results presented here can thus be used to semi-quantitatively determine the organic carbon contents of selected soil forms.
Better together – relative retention time plus spectral matching improves automated glycan characterization using PGC-nLC-IT-ESI-MS/MS

Porous Graphitized Carbon nano-liquid chromatography tandem mass spectrometry (PGC-nLC-MS/MS) is a glycomics technique with the unique capacity to differentiate isobaric glycans. The lack of suitable software tools integrating chromatography and MS-information delivered by PGC-nLC-MS/MS has been limiting fast and robust glycan identification and quantitation. We report a LC-system-independent strategy called GlycoRRT that combines relative retention time (RRT) and negative ion fragment spectra analyses for isobaric structure-specific glycomics of PGC-nLC-MS/MS data. The GlycoRRT toolset is fully customizable and easily adaptable enabling semi-automated high-throughput structural assignments. The current library contains over 200 entries and their individual meta-data (MS instrumentation, experimental conditions, retention times, fragmentation profiles and glycan structural diagnostic ion features) relevant for reliable data analyses. The GlycoRRT workflow was employed to map the N- and O-glycome in blood group matched human plasma and urine as well as decipher Immunoglobulin (IgG) glycosylation features from 13 different animal species. We have also developed visualization tools to enable a consistent, reliable, and reproducible analysis of large sets of multidimensional PGC-nLC-MS/MS glycomics data. This comprehensive glycan resource provides the glycan map of human and animal species, will serve as a reference in dissecting the role of glycans in host pathogen interaction and zoonotic disease transmission.
The amyloid concentric β-barrel hypothesis: Models of amyloid beta 42 oligomers and annular protofibrils

Amyloid beta (Aβ) peptides, a major contributor to Alzheimers disease, occur in differing lengths, each of which forms a multitude of assembly types. The most toxic soluble oligomers are formed by Aβ42; some of which have antiparallel β-sheets. Previously, our group proposed molecular models of Aβ42 hexamers in which the C-terminus third of the peptide (S3) forms an antiparallel 6-stranded β-barrel that is surrounded by an antiparallel barrel formed by the more polar N-terminus (S1) and middle (S2) portions. These hexamers were proposed to act as seeds from which dodecamers, octadecamers, both smooth and beaded annular protofibrils, and transmembrane channels form. Since then, numerous aspects of our models have been supported by experimental findings. Recently, NMR-based structures have been proposed for Aβ42 tetramers and octamers, and NMR studies have been reported for oligomers composed of ~ 32 monomers. Here we propose a range of concentric β-barrel models and compare their dimensions to image-averaged electron micrographs of both beaded annular protofibrils (bAPFs) and smooth annular protofibrils (sAPFs) of Aβ42. The smaller oligomers have 6, 8, 12, 16, and 18 monomers. These beads string together to form necklace-like bAPFs. These gradually morph into sAPFs in which a S3 β-barrel is shielded on one or both sides by β-barrels formed from S1 and S2 segments.
Learning the mutational landscape of the cancer genome

An ongoing challenge to better understand and treat cancer is to distinguish neutral mutations, which do not affect tumor fitness, from those that provide a proliferative advantage. However, the variability of mutation rates has limited our ability to model patterns of neutral mutations and therefore identify cancer driver mutations. Here, we predict cancer-specific mutation rates genome-wide by leveraging deep neural networks to learn mutation rates within kilobase-scale regions and then refining these estimates to test for evidence of selection on combinations of mutations by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and used these maps to identify new putative drivers in understudied regions of the genome including cryptic alternative-splice sites, 5’ untranslated regions and infrequently mutated genes. These results, available for exploration via web interface, indicate the potential for high-resolution neutral mutation models to empower further driver discovery as cancer sequencing cohorts grow.
Effects of Cholesterol on the mechanism of fengycin, a biofungicide

Fengycins are a class of antifungal lipopeptides synthesized by the bacteria Bacillus subtilis, commercially available as the primary component of the agricultural fungicide Serenade®. They are toxic to fungi, but far less to mammalian cells. One key difference between mammalian and fungal cell membranes is the presence of cholesterol only in the former; recent experimental work showed that the presence of cholesterol reduces fengycin-induced membrane leakage. (1) Since our previous all-atom and coarse-grained simulations (2, 3) suggested that aggregation of membrane-bound fengycin is central to its ability to disrupt membranes, we hypothesized that cholesterol might reduce fengycin aggregation. Here, we test this hypothesis using coarse-grained molecular dynamics simulations, with sampling enhanced via the weighted ensemble method. The results indicate that cholesterol subtly alters the size distribution for fengycin aggregates, limits the lateral range of their membrane disordering, and reduces the ability of aggregates to bend the membrane. Taken together, these phenomena may account for cholesterol’s affects on fengycin activity.
Reviews

For the more advanced parts, readers are advised to be “standing by a board, armed with writing implements (and possibly a computer algebra system), prepared to do a fair amount of work and a whole lot of pacing. ” Despite its title, this book is not an extensive review of all things combinatorial: rather it is a delightful exposition giving some important examples of connections between various areas: enumeration, recurrences, combinatorial games, combinatorial designs, and some topics in algebra. Readers are not expected to start on page 5 and plough through to page 327. This is a book that can, and probably should, be digested one chapter at a time, going more or less wherever one takes one’s fancy. Some books take time to come to fruition. Perci Diaconis and Ron Graham’s Magical Mathematics: The Mathematical Ideas That Animate Great Magic Tricks [7] was under contract for twenty-seven years before it finally surfaced to great fanfare in 2011. John H. Conway and Steve Sigur’s The Triangle Book has had a similarly lengthy history, with a less happy outcome so far: both authors (and would-be publisher Klaus Peters) are deceased. It remains to be seen if the visionary manuscript can be resuscitated, polished, and published to share with the wider community. Richard Guy was one of the first people whom John Conway considered bringing in to help with the project following Sigur’s death in 2008. As it happened, Richard had a project of his own that he was trying to see to completion, and the good news is that with Ezra “Bud” Brown’s assistance since 2015, he did just that. Before turning to the details of the resulting tome, The Unity of Combinatorics, it may be worth briefly surveying Richard’s unconventional and very lengthy mathematical life. Richard Guy finished his bachelor’s degree at Cambridge in his native England in 1938, a year before the start of World War II. He didn’t get a “first,” a fact he himself blamed on an over-indulgence in chess. Instead of pursuing a career in research, he initially opted to try teaching, receiving the appropriate diploma from the University
Ray-tracing Simulation and Hybrid Channel Modeling for Low-Terahertz UAV Communications

Unmanned Aerial Vehicles (UAVs) are currently playing an important role in both military and commercial applications. While UAVs possess potential to enhance network coverage with their high flexibility, combining with the Tera-hertz (THz) band is promising to enhance the wireless data rate with abundant spectrum resources and ultra-high bandwidth. As a fundamental challenge, air-ground (AG) channel modeling and analysis in the THz band are investigated in this paper, based on ray-tracing simulation and hybrid modeling approaches. The channel statistics are extracted and analyzed based on ray-tracing simulated data, including path loss, excess delay, among others. In particular, the height dependence of statistical parameters is thoroughly analyzed, which shows a clear trend of increasing channel sparsity with high-altitude UAVs. In light of the simulated data and analysis, a hybrid channel model including the line-of-sight, ground-reflection path, and stochastically generated multi-path, is proposed to characterize the THz AG propagation, for which the fading characteristics are validated.
Achieving Robust Performance for Traffic Classification Using Ensemble Learning in SDN Networks

Software-defined networking (SDN) enables centralized control of a network of programmable switches by dynamically updating flow rules. This paves the way for dynamic and autonomous control of the network. In order to be able to apply a suitable set of policies to the correct set of traffic flows, SDN needs input from traffic classification mechanisms. Today, there is a variety of classification algorithms in machine learning. However, recent studies have found that using an arbitrary algorithm does not necessarily provide the best classification outcome on a dataset, and therefore a framework called ensemble which combines individual algorithms to improve classification results has gained attraction. In this paper, we propose the application of the ensemble algorithm as a machine learning pre-processing tool, which classifies ingress network traffic for SDN to pick the right set of traffic policies. Performance evaluation results show that this ensemble classifier can achieve robust performance in all tested traffic types.
A Security-Constrained Reinforcement Learning Framework for Software Defined Networks

Reinforcement Learning (RL) is an effective technique for building ‘smart’ SDN controllers because of its model-free nature and ability to learn policies online without requiring extensive training data. However, as RL agents are geared to maximize functionality and explore the environment without constraints, security can be breached. In this paper, we propose Jarvis-SDN, a RL framework that constrains explorations by taking security into account. In Jarvis-SDN, the RL agent learns ‘intelligent policies’ which maximize functionality but not at the cost of security. Standard network flow based attack sig-natures obtained from intrusion detection system (IDS) datasets cannot be used as policies because they do not conform to the state model of the RL framework and thus have poor accuracy and high false positives. To address such issue, the security policies for constraining explorations in Jarvis-SDN are learnt in a semi-supervised manner in the form of ‘partial attack signatures’ from packet captures of IDS datasets that are then encoded in the objective function of the RL based optimization framework. These signatures are learnt using Deep Q-Networks (DQN). Our analysis shows that DQN based attack signatures perform better than classical machine learning techniques, like decision trees, random forests and deep neural networks (DNN), for common network attacks. We instantiate our framework for a SDN controller with the goal of intelligent rate control to further analyze the effectiveness of the attack signatures.
Slotted ALOHA for Wireless Powered Resource-Constrained Networks

This paper introduces a Slotted ALOHA protocol targeted at wireless powered communication networks where nodes have very limited resources: each wireless energy transfer (WET) allows only basic operations and a single packet generation and transmission attempt. The hybrid access point (HAP) keeps track of the number of packet transmission attempts over the channel to perform WET once a given threshold value is reached. The numerical results indicate that there exists an optimal threshold value that maximizes throughput for a given number of nodes, packet generation rate, and number of energy packets.
Distributed Load Balancing From the Edge in IP Networks

To improve bandwidth utilization in IP networks, flow aggregates are typically split over multiple paths. In this context, we propose a fully distributed load balancing mechanism that operates only from the edge. Each source is able to determine the split ratios based on already available link state information so as to minimize the maximum link utilization in the network. Without extra signaling, our solution provides a feasible load balancing at each iteration and diminishing returns until convergence to a stable state. Through numerical results on a wide variety of instances, we show that it converges to a near-optimal solution in a few iterations. Thanks to packet-level simulations on an SD-WAN scenario, we also compare its performance in a dynamic environment over centralized and legacy load balancing solutions.
Network Service Embedding in Multiple Edge Systems: Profit Maximization by Federation

A service chain (SC) comprises a series of service functions realized as virtualized network functions (VNFs). Each VNF has specific resource, bandwidth, and location requirements. This study assumes multiple SCs to be placed on geo-distributed heterogeneous edge servers owned by multiple edge service providers (ESPs). Each ESP selectively hosts SCs to earn profit. We consider the problem to maximize the total profit (i.e., social welfare) of all ESPs by organizing ESPs into a set of disjoint federations called federation structure. We formulate the problem of finding an optimal federation structure, identify the difficulties in solving the problem, and present two time-efficient heuristics as our approach. Simulation results show that elaborate federation structures do bring in higher social welfare compared with simple all-in or all-out ESP organization. The proposed heuristics can also approximate the optimal result in many cases.
Renewable energy from two-stage anaerobic digestion of organic wastes

Experimental studies of two-phase anaerobic digestion of corn steep liquor (a waste product from processing corn grain for starch extraction) were performed. For the first time, an automatic mode of operation was implemented in continuous processes with simultaneous production of hydrogen and methane using the developed computer system for monitoring and control of pilot bioreactors. The best results for daily biogas yield were obtained at an organic load of 20 g/L for hydrogen producing bioreactor, as the daily yield of biohydrogen is 0.74 dm3/L at a hydrogen concentration in biogas of 32.6% (0.24 dm3/L pure biohydrogen) and the daily biomethane yield in the methane producing bioreactor is 0.587 dm3/L at a methane concentration in biogas of 69% (0.4 dm3/L pure biomethane). The obtained energy is more than 40% compared to the traditional single-stage methane production process. A disadvantage is the need for additional purification from hydrogen sulfide of both gases before obtaining electricity and/or heat.
Educational Set-up for Brushless Motor Drives

Traditionally the brushed DC motor is the first motor that students encounter in a laboratory assignments on electrical drives. However a number of simplifications are taken into account when explaining the workings of the brushed DC motor. Considering basic knowledge of electromagnetic principles, the attraction between a magnet and a current carrying winding is easily accepted by students. A Brushless DC motor drive, which is based on this electromagnetic principle, is an easy to understand motor drive when it comes to the control of the motor. All underlying physical properties of the brushless DC motor drive are easier to visualize compared to the brushed DC motor with mechanical commutator. Therefore this motor drive more is suited as an introductory course in motor drives. Virtual online experiments as well as lab assignments are demonstrated that visualize every aspect of the brushless motor drive. The basic models for simulation and animation are presented and a typical laboratory set-up is demonstrated.
An approach to bridge the gap between ubiquitous embedded devices and JFML: A new module for Internet of Things

Internet of Things enables sensors and actuators to share heterogeneous data between different devices. Such data can be used to create intelligent systems to control diverse structures available in houses, cities, or industrial environments among others. In this context, one of the most used approaches to handle these intelligent systems is based on Fuzzy Rule-Based Systems (FRBS) due to their suitability for addressing complex data and managing their imprecision. However, most of the current developments in this area are usually ad-hoc solutions limited by the intercommunication between FRBS and IoT devices. This results into significant challenges in reusing these solutions to solve latent problems. To bridge this gap, a new module for the open source library JFML is proposed to offer a complete implementation of an IoT infrastructure to develop intelligent IoT solutions based on the IEEE std 1855–2016. Moreover, a case study with real IoT devices is presented to showcase the use of the proposed module.
A pH-responsive polymer based on dynamic imine bonds as a drug delivery material with pseudo target release behavior

In this study, pentaerythritol tetra(3-mercaptopropionate)-allylurea-poly(ethylene glycol) (PETMP-AU-PEG), produced by the Schiff-base reaction between terminal-aldehyded PEG and PETMP-AU, was used to prepare doxorubicin (DOX)-loaded polymers for triggered release. The DOX-loaded PETMP-AU-PEG polymers had sizes of about 200 nm, non-toxicity and highly pH-sensitive properties. An in vitro release study confirmed that the polymer could release DOX rapidly by cleavage of acid-labile imine bonds of the polymer. The release of DOX at neutral pH was very slow. However, in a weakly acidic environment (pH 6.8 and pH 6.0), it released much faster within the first 2 h. Fluorescence microscopy further verified that DOX released from PETMP-AU-PEG polymers could be pH sensitive. According to the results of cytotoxicity determination and microscopy imaging of U2OS cells (human osteosarcoma, tumor cells) and MC3T3-E1 Subclone 14 cells (normal tissue cells), the DOX-loaded polymers could maintain high stability in the neutral environment while they release DOX rapidly in response to the very weakly acidic environment. Moreover, it is more important that the U2OS could be inactivated in the micro-acidic environment produced by its own growth, even in a neutral environment, without any chemical reagent addition.