Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrospun Silk Fibroin Scaffolds for Tissue Regeneration: Chemical, Structural, and Toxicological Implications of the Formic Acid-Silk Fibroin Interaction

The dissolution of Bombyx mori silk fibroin (SF) films in formic acid (FA) for the preparation of electrospinning dopes is widely exploited to produce electrospun SF scaffolds. The SILKBridge® nerve conduit is an example of medical device having in its wall structure an electrospun component produced from an FA spinning dope. Though highly volatile, residual FA remains trapped into the bulk of the SF nanofibers. The purpose of this work is to investigate the type and strength of the interaction between FA and SF in electrospun mats, to quantify its amount and to evaluate its possible toxicological impact on human health. The presence of residual FA in SF mats was detected by FTIR and Raman spectroscopy (new carbonyl peak at about 1,725 cm−1) and by solid state NMR, which revealed a new carbonyl signal at about 164.3 ppm, attributed to FA by isotopic 13C substitution. Changes occurred also in the spectral ranges of hydroxylated amino acids (Ser and Thr), demonstrating that FA interacted with SF by forming formyl esters. The total amount of FA was determined by HS-GC/MS analysis and accounted for 247 ± 20 μmol/g. The greatest part was present as formyl ester, a small part (about 3%) as free FA. Approximately 17% of the 1,500 μmol/g of hydroxy amino acids (Ser and Thr) theoretically available were involved in the formation of formyl esters. Treatment with alkali (Na2CO3) succeeded to remove the greatest part of FA, but not all. Alkali-treated electrospun SF mats underwent morphological, physical, and mechanical changes. The average diameter of the fibers increased from about 440 nm to about 480 nm, the mat shrunk, became stiffer (the modulus increased from about 5.5 MPa to about 7 MPa), and lost elasticity (the strain decreased from about 1 mm/mm to about 0.8 mm/mm). Biocompatibility studies with human adult dermal fibroblasts did not show significant difference in cell proliferation (313 ± 18 and 309 ± 23 cells/mm2 for untreated and alkali-treated SF mat, respectively) and metabolic activity. An in-depth evaluation of the possible toxicological impact of residual FA was made using the SILKBridge® nerve conduit as case study, following the provisions of the ISO 10993-1 standard. The Potential Patient Daily Intake, calculated from the total amount of FA determined by HS-GC/MS, was 2.4 mg/day and the Tolerable Exposure level was set to 35.4 mg/day. This allowed to obtain a value of the Margin of Safety of 15, indicating that the amount of FA left on SF mats after electrospinning does not raise concerns for human health.
Legacy Effects of Contrasting Long-Term Integrated Weed Management Systems

To reduce reliance on herbicides and maintain crop productivity, integrated weed management (IWM) seeks to optimize synergies between diverse sets of weed management practices combined at the cropping system scale. Nevertheless, data on weed community response to the long-term implementation of IWM practices remain scare. Here, we assessed the effects of four IWM systems with contrasting objectives and practices (S2: transition from superficial tillage to conservation agriculture; S3: no-mechanical weeding; S4: mixed mechanical and chemical weeding; S5: herbicide-free; all with 6 year rotations) compared to a conventional reference (S1: herbicide-based with systematic plowing and a 3 year rotation) on taxonomic and functional weed community composition and structure after 17 years of continuous implementation. We examined the legacy effects of these systems with a uniformity trial consisting of winter wheat managed uniformly across the systems as well as with a novel in situ weed seedbank approach involving tilled strips. We found that resulting weed communities in IWM systems were more species rich (species richness from 1.1 to 2.6 times greater) and more abundant (total density from 3.3 to 25 times greater) than those observed in the reference system, and differed in term of taxonomic and functional composition. In addition, we found that, when systems shared the same weed species, germination patterns of two thirds of the species differed between systems, highlighting the selection pressures some IWM practices exert on weeds. We showed that analyzing the superficial germinable seedbank in situ with tilled strips could provide a comprehensive view of resulting weed communities and be helpful in developing cropping systems that foster agroecological weed management.
Is a Little Enough? Paucity of Immune Proteins in Serum of Precocial Neonates of a Marine Carnivoran—the Atlantic Grey Seal

Mammalian mothers usually provide their offspring with large quantities of immunoglobulins (antibodies) for circulation in blood, either trans-placentally before birth, via colostrum briefly thereafter, or, less commonly, from milk. Neonates of true, phocid seals, however, are peculiarly impoverished in serum immunoglobulins, the levels of which slowly increase but do not reach adult levels by the time of weaning. We investigated whether grey seal (Halichoerus grypus) neonates compensate through an elevation or rapid maturation in levels of serum innate immune factors, namely acute phase and complement proteins. Instead, their sera contained remarkably low levels of acute phase proteins (including C-reactive protein, haptoglobin, hemopexin, ceruloplasmin, orosomucoid), compared to their mothers, that barely increased to adult levels by weaning. For complement, there was a strong demarcation between the early activation and amplification cascade components (present at normal adult levels in pups) and the late lytic membrane attack complex and regulatory proteins (consistently at low relative levels). Phocid neonates therefore differ dramatically from land Carnivorans, such as dogs and cats, in early life immune protection. That neonatal phocids survive this apparent vulnerability to infections between birth and weaning prompts questions as to what other mechanisms protect them, and the adaptive value of their seeming vulnerability.
Language, Childhood, and Fire: How We Learned to Love Sharing Stories

Stories do not fossilize. Thus, exploring tales shared during prehistory, the longest part of human history inevitably becomes speculative. Nevertheless, various attempts have been made to find a more scientifically valid way into our deep human past of storytelling. Following the social brain hypothesis, we suggest including into the theory of human storytelling more fine-grained and evidence-based findings (from archaeology, the cognitive sciences, and evolutionary psychology) about the manifold exaptation and adaptation, genetic changes, and phenotypic plasticity in the deep human past, which all shaped the emergence of storytelling in hominins. We identify three preconditions for humans sharing stories: first, the long evolution of language in the different taxa as one of the preconditions of ostensive signaling; second, the pivotal role of childhood in the evolution of collaborative intentionality; and third, the role of fireside chats in the rise of elaborative (i.e., narrative) sharing of stories. We propose that humans, albeit perhaps no other hominins learned to understand others through sharing stories, not only as intentional agents, but also as mental ones.
Over-Expression and Prognostic Significance of FATP5, as a New Biomarker, in Colorectal Carcinoma

Background: Fatty acid transporters (FATPs) family play an important role in the uptake and metabolism regulation of long-chain fatty acids, which influence the occurrence and developing of multiple tumors. Fatty acid transporter 5(FATP5), a member of FATPs family, participates in fatty acid transport and lipid metabolism and is related to tumor development, whose mechanism in colorectal cancer (CRC) remains unclear. Methods: In this study, we comprehensively utilized a range of relevant bioinformatic tools along with multiple databases to analyze the expression of FATPs family and investigate the biological function and prognostic value of FATP5 in CRC. Besides, cell proliferation and cell cycle distribution analysis, western blotting and immunohistochemistry (IHC) further validated the conclusion of bioinformatics analysis. Results: FATP5 is the only member of FATPs family which was overexpressed in CRC. In the survival analysis based on the GSE39582 databases, the low expression of FATP5 predicts poor prognosis in CRC. Similar results were also observed in GSE17536, GSE28814 and TCGA colon cohorts. The potential function of DNA methylation regulated the abnormal expression of FATP5 in CRC. In addition, enrichment analysis indicated that FATP5 also participates in the regulation of cell cycle. Furthermore, Gene Set Enrichment Analysis (GSEA) showed a strong negative correlation between FATP5 and cell growth, implying that it may participate in regulating cancer cell proliferation by the regulation of cell cycle G2/M transition. At last, we identified that FATP5 was overexpressed in colorectal carcinoma tissues through immunohistochemistry staining, and played an important role in cell cycle by cell proliferation and cell cycle distribution analysis. Conclusion: This study suggested that FATP5 was overexpression in colorectal carcinoma and predicted favorable prognosis, indicating it as a novel appealing prognostic marker for CRC.
Editorial: The Expansion of Female Fertility

1 Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel, Division of Obstetrics and Gynecology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel, Department of Obstetrics and Gynecology, Hadassah Mount Scopus-Hebrew University Medical Center, Jerusalem, Israel, Department of Obstetrics and Gynecology, Carmel Medical Center, Haifa, Israel, Department of OBGYN, Faculty of Medicine Technion, Haifa, Israel
Collaborative Internet of Things (C-IoT) Data Analysis for Enhancing Activity Recognition and Preventing Serious Health Problems

Collaborative Internet of Things (C-IoT) is an emerging paradigm that has generated a large amount of accessible and usable data in real-time, constituting an interesting tool for decision-making problems. The paper presents a healthcare data analytics approach based on collecting and analyzing data from C-IoT. We first provide an architectural plan involving several distributed sites collaborating to collect necessary data for patient's health conditions monitoring. We use for that well-known IoT technologies like Radio Frequency IDentification (RFID), Near Field Communication(NFC), Beacons and Ambient Assisted Living sensors. We then learn on such data to create adequate patient profiles and use the Pearson Correlation Coefficient (PCC) over a long period of time data to detect potential health risks. Finally, decisions are made regarding the patient's condition according to the symptoms/diseases detected.
Differences in learning engagement, academic workload, and technology use during online learning: a comparison among primary, middle, and high school students

Most of the existing research studies on K-12 education usually regard K-12 as a whole, but ignore the differences of grades and phases. In fact, students at different school phases have distinct characteristics and behavior habits such as their cognitive development, online learning experiences, workload, etc., which may affect their achievements of learning goals in online learning. This study examined the differences in learning engagement and academic workload among elementary, middle, and high school students during online learning, and analyzed their preferences and habits of technology uses. The results revealed significant differences in student engagement and academic workload among the three phases. Middle school students had the highest scores of engagements, while high school suffered most academic workloads. These findings are expected to extend our understanding of K-12 online education in the context of China.
Addressing Exposure Bias in Uplift Modeling for Large-scale Online Advertising

Uplift modeling is an important task for online advertising and marketing. Advertisers rely on accurate modeling of the uplift effect to formulate, plan and implement their advertising and marketing strategies. Therefore, the capability of effective and efficient uplift modeling is essential for advertising platforms to attract and satisfy their customers (i.e., advertisers). In practical advertising applications, uplift modeling focuses on the estimation of the uplift effect caused by ad exposure. It is not a trivial task to estimate such causal impact of ad exposure at the individual level. In this paper, we propose an end-to-end approach for explicit uplift modeling, using data collected from Randomized Controlled Trials (RCTs) in large-scale real-world advertising platforms. More specifically, we first introduce the Explicit Uplift Effect Network (EUEN) to explicitly model the uplift effect and demonstrate its advantages in uplift modeling. Then for the exposure uplift effect modeling, we further propose the Explicit Exposure Uplift Effect Network (EEUEN), which can correct the exposure bias for uplift modeling. We evaluate our proposed approach with both public data sets as well as data sets collected from our advertising platform. The significant improvements with respect to various performance metrics demonstrate the advantages of our approach.
Optimal Sizing of Hybrid Wind-Storage System for Load Shifting

With the increasing penetration of renewable energy in power system, energy storage systems (ESSs) are expected to undertake the responsibility for load shifting, so as to reduce the burden of traditional generators. However, the improper size of an ESS will lead to an undesired cost even cause instability. In order to maximize total profits of hybrid wind-storage systems, an economic sizing method for an ESS is proposed in this paper, considering the cycle life loss of an ESS To address above, the particle swarm optimization algorithm is employed to jointly optimize the capacity of an ESS and the coefficient of load shifting. With the help of the proposed method, both the operation cost and greenhouse gas emissions of the hybrid wind-storage system are reduced. The simulation results demonstrate the effectiveness of proposed method.
Aviation Turbulence on Indonesia and The Relationship to Himawari-8 Weather Satellite Imagery

Aviation turbulence is one of the dangerous events of a flight. This hazard impacts people and aircraft. One source of information on turbulence events comes from the NTSC report. Then one of the causes of flight turbulence is the presence of convective clouds. Indonesia, which is located in the tropics, is an area that is very active in its convective activities. Descriptive analysis was used in this study. There were three aviation turbulence events in 2016-2020, namely on 4 May 2016, 6 May 2016, and 24 October 2017. All incidents of aviation turbulence occurred wind shear around the incident location based on upper-air observation data. In two turbulence events, there are convective clouds around the scene.
An Improved Strategy Combining with MPC for IPMSM Flux-Weakening Control

This paper proposes a new hybrid flux-weakening strategy which combines look-up table algorithm, voltage feed-back and model predictive control method to realize the high speed control of interior permanent synchronous motor (IPMSM). In order to identify the current situation of the motor in the process of speed increase, a large number of experiments have been carried out. And by processing these data with Matlab fitting tools, two precise three-dimensional fitting planes are obtained. In the other place, for the stable control which aims to avoid voltage exceeding limit, feedback means is also taken. Most of all, model predictive control is integrated into this strategy, so that selecting the optimal control voltage vector for IPMSMs. The effectiveness of this strategy is verified by simulation.
Short-Time Scale Mode Transition Control of the Four-Switch Buck-Boost Converter

Four-switch Buck-Boost converters are widely used in applications with a large range of input and output voltages. The typical challenge of these converters is the mode transition control, which may result in the instability of output voltages, and could even damage the system. To solve this problem, existing research relies on various multimode control strategies to deal with smooth mode transitions in a relatively long-time scale. However, for applications such as DC microgrid that require faster mode transitions, an improved multimode control strategy with a duty cycle compensation method is proposed. A short-time scale mode transition is achieved, and the output voltage is maintained stable as well. Specifically, the basic principle, small-signal analysis, and the controller design are presented. The effectiveness and correctness of the proposed control are verified by simulations and experiments.
Robust Predictive Control of Parametric Uncertainties in Islanded DC-Microgrids

This work proposes a robust predictive control strategy against inductive parametric uncertainties through model predictive control with integral action in islanded Direct current microgrid systems (DCMS). DCMS consist of numbers of parallel power converters to share load currents through the inductance of DC/DC converters. However, the inductance parameters are dependent on the physical implementation of the system, and their values may not match their nameplate. Such disparities may lead to unequal responses of the system, which can potentially reduce the performance of the DCMS operation. First, the system model is generated based on the nominal values of the system. The model is transformed into parametric unmatch uncertainty to form a robust control problem, which is then translated into a model predictive control problem. The inductance variations are stabilized with the uncertainty dynamic algebraic Riccati equation through control law. The close-loop was generated with integral action to eliminate an error on DC-grid voltage for a wide range of parameter variations. The proposed approach not only eliminates an error on DC-grid voltage also ensures equal sharing load. Fast response and better performance were observed in the simulation results.
Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment

The detection, location, and behavior recognition of human beings in different environments is not only a subject of a wide range of studies, but has also triggered the development of a large number of applications, including those which enhance sustainability and efficiency of infrastructures. For instance, the estimation of the occupancy could improve the energy management of a building. Due to human presence or movement over a particular area, the analysis of variations in wireless signal properties of already deployed wireless technology such as WiFi systems provides the information needed for Machine Learning models to accomplish the non-intrusive (device-free) detection and classification of different human activities. In this context, this work focuses on detecting seated people in an indoor scenario by using ensemble learning, a particular branch of Machine Learning models for supervised learning that hinges on combining the outputs of individual predictors. Furthermore, we evaluate the transferability of the knowledge modeled by ensemble learners. When trained in a particular frequency or channel, such models are used to classify data captured over another different frequency. Our experimental setup and discussed results reveal that while ensembles attain satisfactory levels of predictive accuracy predictions, their knowledge cannot be transferred among different frequencies. This conclusion opens an exciting future towards new means to perform effective knowledge transfer over the frequency domain.
Neutral-point potential balance control for three-level Z-source inverters based on double modulation wave technique

Conventional neutral point clamped (NPC) three-level Z-source inverters are commonly powered by two isolated DC sources. For a topology composed of a DC source and two cascaded capacitances, the relationship between two shoot through states and neutral-point potential is introduced, and the basic principle of double modulation wave carrier-based PWM (DMWPWM) is analyzed. A neutral-point potential control method of three-level Z-source inverter based on DMWPWM is proposed. The position of shoot through is designed rationally and the intermediate voltage modulation wave is adjusted dynamically. The feedback control method is adopted to control the neutral point potential, which eliminates the low-frequency oscillations of NP potential and restrains the DC offset effectively, and won’t increase the number of semiconductor commutations. The superiority of the proposed method is verified by simulation.
A Methodology for Human-Centred IoT Collectives Based on Socio-Ethical Policies

An emerging aspect to consider during human-device interaction is addressing ethical concerns about smart, internet-connected devices and making them behave in a more human-centred manner, i.e., ethically and in a socially acceptable manner. We propose that smart devices can be made to behave in a more human-centred manner, by the application of a set of policies to their fundamental operations, if a technique for defining their key roles and mapping ethical and administrative policies to them is developed. This paper proposes a methodology which consists of four primary phases, including concept development, defining and mapping policies, implementing the processing of policies and deploying the devices. Our suggested methodology may be used in a variety of situations where smart devices interact with people. For illustration, we have applied the proposed methodology to a supermarket scenario where each phase defined in the methodology has been followed.
Near-real-time Anomaly Detection in Encrypted Traffic using Machine Learning Techniques

In the last decade, the adoption of HTTPS for securing Internet communications increased by up to 90%. Threat actors adapted to this transition to HTTPS by writing more sophisticated malware that encrypt their communications with command-and-control centers. On the other hand, network security appliances are limited by the impossibility of inspecting packet payloads for deeper investigations. In this paper, we propose a cybersecurity analytics which monitors encrypted network flows and extracts features to detect possible occurring attacks and anomalies, by combining machine learning with a statistical approach. The analytics is embedded in a network security monitoring platform, named ararnis®, which provides cybersecurity analysts with a comprehensive overview of the monitored network and its traffic to support them in the identification of potentially malicious activities taking place. The detection capabilities of the proposed analytics have been tested both on a benign and a malicious dataset. This latter has been assembled by our security analysts and includes packet captures of samples and tools, respectively, developed and used by worldwide leading threat actors. Results show 96.6% accuracy on the malicious dataset, with a false positive rate approximatively equal to 0.001% when the analytics monitors legitimate encrypted network traffic.