Miracle-dz/UniFAR-FTUs huggingface dataset:
Considering Strain Variation and Non-Type Strains for Yeast Metabolic Engineering Applications

A variety of yeast species have been considered ideal hosts for metabolic engineering to produce value-added chemicals, including the model organism Saccharomyces cerevisiae, as well as non-conventional yeasts including Yarrowia lipolytica, Kluyveromyces marxianus, and Pichia pastoris. However, the metabolic capacity of these microbes is not simply dictated or implied by genus or species alone. Within the same species, yeast strains can display distinct variations in their phenotypes and metabolism, which affect the performance of introduced pathways and the production of interesting compounds. Moreover, it is unclear how this metabolic potential corresponds to function upon rewiring these organisms. These reports thus point out a new consideration for successful metabolic engineering, specifically: what are the best strains to utilize and how does one achieve effective metabolic engineering? Understanding such questions will accelerate the host selection and optimization process for generating yeast cell factories. In this review, we survey recent advances in studying yeast strain variations and utilizing non-type strains in pathway production and metabolic engineering applications. Additionally, we highlight the importance of employing portable methods for metabolic rewiring to best access this metabolic diversity. Finally, we conclude by highlighting the importance of considering strain diversity in metabolic engineering applications.
A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.
Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm

This article introduces a benchmarking framework that allows rigorous evaluation of parallel model-based optimizers for expensive functions. The framework establishes a relationship between estimated costs of parallel function evaluations (on real-world problems) to known sets of test functions. Such real-world problems are not always readily available (e.g., confidentiality and proprietary software). Therefore, new test problems are created by Gaussian process simulation. The proposed framework is applied in an extensive benchmark study to compare multiple state-of-the-art parallel optimizers with a novel model-based algorithm, which combines ideas of an explorative search for global model quality with parallel local searches to increase function exploitation. The benchmarking framework is used to configure good batch size setups for parallel algorithms systematically based on landscape properties. Furthermore, we introduce a proof of concept for a novel automatic batch size configuration. The predictive quality of the batch size configuration is evaluated on a large set of test functions and the functions generated by Gaussian process simulation. The introduced algorithm outperforms multiple state-of-the-art optimizers, especially on multimodal problems. Additionally, it proves to be particularly robust over various problem landscapes, and performs well with all tested batch sizes. Consequently, this makes it well suited for black-box kinds of problems.
Sociodemographic variables affecting caregivers’ attitudes towards the provision of healthy breakfast and lunchboxes to children in their care

Objective: A study was undertaken to determine the sociodemographic variables (SDV) and caregivers’ attitudes that impact on healthy eating and the provision of healthy breakfast and school lunchboxes. Design: This was a cross-sectional, descriptive study using self-administered questionnaires. Setting: Participating Quintile 5 primary schools in Bloemfontein, South Africa (SA). Participants: Caregivers of learners, aged 6–12 years (N = 1 286). Main outcome measures: To determine associations between sociodemographic variables (SDV) and attitudes towards the provision of healthy breakfast and lunchbox foods. Analysis: Wilcoxon two-sample test for unpaired data or the chi-square test to compare groups. A p-value of <0.05 indicated statistical significance. Results: Caregivers with higher income and those living with a life partner were more likely to perceive healthy food as being more expensive than less healthy food (p < 0.01 and p < 0.01, respectively) and were of the opinion that preparing lunchboxes increased their workload (p < 0.01 and p < 0.01, respectively). Caregivers with lower income had a less positive attitude towards healthy eating habits (p < 0.01). Conclusions: Caregivers mostly had a positive attitude towards providing healthy breakfast and lunchbox foods. Discrepancies between caregivers’ perception of preparation time and cost of healthy food, and the provision of healthy food to learners, were observed.
Does Less Pain Predict Better Quality of Life among Malaysian Patients with Mild–Moderate Knee Osteoarthritis?

This study aims to identify the relationship between knee functional status and Health-Related QoL (HRQoL) in mild to moderate knee osteoarthritis (OA) patients and to ascertain which subdomain of knee functional status best predicts good HRQoL. A cross-sectional study was conducted in an orthopaedic clinic of a tertiary hospital in Malaysia. Patients aged 40–75 years old with mild–moderate primary knee OA were recruited. The Knee Injury and Osteoarthritis Outcome Score (KOOS) and SF-36 questionnaires were used to measure knee functional status and HRQoL, respectively. Subdomains of KOOS include “function in daily living”, “function in recreational activities”, “pain”, “symptom”, and “knee-specific quality of life”. Subdomains for SF-36 are Physical Component Summary (PCS) and Mental Component Summary (MCS). Overall, 290 patients fulfilled the inclusion criteria of the study, with a mean age of 66.8 years old (±7.06). Majority were female (57.6%) and Malay (79.7%). The relationships between all KOOS and HRQoL subdomains were significant. “Pain” contributed most towards better physical HRQoL ((PCS) Adj. B (95% CI); 0.063 (0.044, 0.169)), while “function in daily living” contributed most towards better mental HRQoL ((MCS) Adj. B (95% CI); 0.624 (0.478, 0.769)). Thus, better HRQoL was related to better pain control and improved “function in daily living” in these patients.
Work Balancing vs. Load Balancing for Network IDS Parallelization

Signature-based Network Intrusion Detection Systems (NIDS) is state-of-the-art for precise attack detection. Using multiple instances of NIDS in parallel is considered the most promising solution for improving its processing speed in the scale of high speed network. This can be realized by (1) distributing the network traffic between multiple NIDS to reduce the network load per system or (2) distributing the signatures (rules) between multliple NIDS to reduce the work load per packet. In this paper, we study distribution strategies targeting application and transport layer for both traffic and rule distribution approaches. In addition, we investigate the importance of considering the processing speed optimization in the rule development phase. Our experiments show that in general traffic distribution performs slightly better in terms of packet drop and alert detection compared to rule distribution. The Transport layer distribution strategy shows traffic distribution parallelization detecting 1.6% more alerts and dropping 6% less packets. We also show that optimizing the rules sets further improves the processing speed significantly.
The relationship between prevalence of dental anxiety and dental caries: cross-sectional study

Department of Biostatistics. Istanbul, Turkey. ABSTRACT Objective: This cross-sectional study aimed to assess level of DA among dental students in different grades and patients and to evaluate associations between DA and presence of dental caries. Material and Methods: Both four hundred all-grade dental students of Istanbul University and patients of Istanbul University Hospital were included in the study after random selection. Data regarding gender, oral hygiene habits, frequency of dental visits, and educational level were obtained via questionnaire. DA of participants were measured using both Modified Dental Anxiety Scale (MDAS) and Dental Fear Survey (DFS). The prevalence of caries was determined using International Caries Detection and Assessment System Criteria (ICDAS). Data were analyzed using Kruskal-Wallis, Mann-Whitney U and Chi square tests. Results: Female patients and female 4th-year students had significantly higher MDAS scores (p<0.05). First-year students had significantly higher MDAS and DFS scores than 4th and 5th-year students (p<0.05). Patients who had caries and brushed their teeth less than twice a day had significantly higher MDAS scores (p<0.05). Among 3rd-year students, individuals who visited dentist regularly had lower MDAS scores (p<0.05). Conclusion: DA among senior dental students was lower than that in younger students with an increase in knowledge about dentistry. Tooth brushing frequency and caries prevalence was associated with DA in patient group. No association was found between DA and frequency of dental
Smart Zones to Create the Ambience of Life

To design “background nonplayer characters” that breathe ambient life into a virtual environment, we propose new concepts that facilitate the creation of Living Scenes. The aim is to generalize their use in video games as a way to improve the feeling of presence for the player. This chapter introduces the concept of Smart Zones used to design credible, consistent, and interactive ambient life, involving autonomous and adaptive NPCs.
Reducing the jamming effects in multicellular Massive MIMO systems

As Massive multiple‐input multiple‐output (Ma‐MIMO) is one of the critical 5G technologies, security aspects of this technology in terms of powerful distributive jamming attacks for improving the total system performance are so important. In this paper, we evaluate the sum spectral efficiency (SE) of Ma‐MIMO systems in the multicell scenario concerning jamming effects. First, the closed‐form expression for sum SE using two detectors maximum ratio combining (MRC) and zero‐forcing (ZF) in the presence of a jammer was obtained. Next, the effects of jamming power, the number of base station antennas, the signal‐to‐noise ratio (SNR), and coherence block lengths on the system performance were obtained. Finally, to improve the signal‐to‐noise and interference ratio (SINR) of cell edge users, the problem of optimizing the fairness trade‐off between the central legitimate users and the cell edge legitimate users is modeled by using log barrier functions. We propose an alternating algorithm to solve the non‐convex problem. Numerical results showed that by considering the same interference power and the power of the legitimate users, the sum SE improved with proposed schemes and also by increasing the number of antennas at BS. Also, the ZF outperforms MRC with increasing the number of antennas. Finally, it is shown that by using the optimal legitimate users instead of overall legitimate users, the system performance in terms of fairness sum SE trade‐off improved.
First Principle Study on Electronic and Transport Properties of Finite-Length Nanoribbons and Nanodiscs for Selected Two-Dimensional Materials

Using the density functional theory, we calculate electronic states of various nanoribbons and nanodiscs formed from selected two-dimensional materials, such as graphene, silicene, and hexagonal boron nitride. The main objective of the analysis is a search for zero-energy states in such systems, which is an important issue as their presence indicates certain topological properties associated with chirality. The analysis is also supported by calculating transport properties.
Implementing intergenerational learning in a preschool: a case study from Taiwan

ABSTRACT The labor shortage in Taiwan has left citizens with little time to care for young children and older adults at home. However, young children and older adults can learn to care for one another through intergenerational learning. This case study investigated intergenerational learning in a preschool to determine the motivations and design elements of its curriculum and the challenges encountered during implementation. The motivation was to enable young children to learn from the experiences of older adults and to care for those around them. The curriculum was thematic and based on interactivity. The challenges were insufficient human resources, funds, reference materials, and knowledge of intergenerational learning and curriculum design as well as parental concerns regarding the adult day care center and the health of the older adults involved. The young children and older adults exchanged life experiences and changed their original perceptions of each other. Their interaction created a feeling of mutual love. This study also provides suggestions for implementing intergenerationallearning in Taiwanese preschools and for increasing awareness of this topic.
Human-Lead-Platooning Cooperative Adaptive Cruise Control

In this study, a Human-Lead-Platoon CACC ((HLP-CACC) controller is proposed for connected and automated vehicles to “include” human drivers in platooning process. The goal is to form a platoon between automated vehicles and human drivers so that turbulence caused by human drivers could be smoothed out by automated vehicles. Unlike the conventional CACC where only longitudinal control is automated, the proposed HLP-CACC regulates both longitudinally and laterally. In other words, the followers in an HLP-CACC platoon are fully autonomous. The controller is formulated utilizing model predictive control (MPC) solved by Chang-Hu’s method. The technology has the following advantages: 1) take advantage of human drivers’ perception to enable conditional full autonomy; 2) accommodate actuator delay in system dynamics to improve actuator control accuracy; 3) automates both longitudinally and laterally; and 4) ensures string stability in partially connected and automated vehicles environment. Both simulation tests and field tests were conducted to verify the effectiveness of the proposed algorithm. Four scenarios, including straight cruising, lane changing, U-turn and circling were tested. Sensitivity analysis was conducted for speed, turning radius, communication delay and oscillation acceleration. The results confirm that the proposed CACC controller is ready for field implementation. The computation time of the proposed optimal control is approximately $4~\sim ~8$ milliseconds when running on an NVIDIA Drive PX 2 computer. Under the control of the proposed HLP-CACC, maximum longitudinal error and lateral error are both within 40 cm.
Effects of Transcranial Direct Current Stimulation of Bilateral Supplementary Motor Area on the Lower Limb Motor Function in a Stroke Patient with Severe Motor Paralysis: A Case Study

In patients with severe motor paralysis, increasing the excitability of the supplementary motor area (SMA) in the non-injured hemisphere contributes to the recovery of lower limb motor function. However, the contribution of transcranial direct current stimulation (tDCS) over the SMA of the non-injured hemisphere in the recovery of lower limb motor function is unclear. This study aimed to examine the effects of tDCS on bilateral hemispheric SMA combined with assisted gait training. A post-stroke patient with severe motor paralysis participated in a retrospective AB design. Assisted gait training was performed only in period A and tDCS to the SMA of the bilateral hemisphere combined with assisted gait training (bi-tDCS) was performed in period B. Additionally, three conditions were performed for 20 min each in the intervals between the two periods: (1) assisted gait training only, (2) assisted gait training combined with tDCS to the SMA of the injured hemisphere, and (3) bi-tDCS. Measurements were muscle activity and beta-band intermuscular coherence (reflecting corticospinal tract excitability) of the vastus medialis muscle. The bi-tDCS immediately and longitudinally increased muscle activity and intermuscular coherence. We consider that bi-tDCS may be effective in recovering lower limb motor function in a patient with severe motor paralysis.
Pathways to Performance: How Communication Technologies Help Bridge Gaps in Musical Culture

This thesis and documentary film proposal explores how cultural definitions of music restrict certain populations from full musical participation and how new communication technologies are helping provide marginalized populations with access to musical expression. Music is a vital human communication form and limiting access from certain people is a social injustice. Innovation in ideology and practices, and the adoption of advanced technologies, help reduce social access barriers to musical participation for all people. This thesis and documentary film proposal examines historical evidence of how marginalized musicians and musical forms together have worked to confront cultural boundaries and enter mainstream disciplined and popular musical culture. Modern technologies help foster musical experimentation and, in a rapidly expanding digital economy, are offering new modes of musical expression to underserved audiences. This thesis proposes that using technology to extend musical practice to new audiences, and loosening social definitions that govern music, will make musical culture more inclusive and socially just than ever before.
7-Ketocholesterol-Induced Micro-RNA-107-5p Increases Number and Activity of Osteoclasts by Targeting MKP1

Osteoclasts (OCs), which are responsible for bone resorption, play a critical role in cholesterol-induced bone loss and recent studies have suggested that various micro-RNAs (miRs) contribute to modulating OCs. We hypothesized that 7-ketocholesterol (7-KC), a metabolite responsible for cholesterol-induced bone loss, induces miR-107-5p, which affects OCs. Overexpression and knock-down of miR-107-5p were performed using miR-107-5p mimic and anti-miR-107-5p, respectively. The effects of miR-107-5p on OCs were analyzed by tartrate-resistant alkaline phosphatase staining, qPCR, and Western blot. MiR-107-5p was upregulated after 7-KC exposure in receptor activator of nuclear factor kappa-Β ligand-stimulated OCs. Furthermore, miR-107-5p upregulation was also observed in tibiae from an atherogenic diet-fed mice compared with mice fed with a normal diet. MiR-107-5p overexpression enhanced the area and number of OCs, whereas inhibiting the endogenous expression of miR-107-5p generated by 7-KC had the opposite effect. Among the possible candidates, mitogen-activated protein kinase phosphatase-1, a stress-responsive dual-specificity phosphatase that inactivates mitogen-activated protein kinase (MKP1), has been proven to be a target gene of miR-107-5p, as demonstrated by the direct interaction between miR-107-5p and the 3′-untranslated region of MKP1. Collectively, our findings demonstrate that 7-KC-induced miR-107-5p promotes differentiation and function of OCs by downregulating MKP1.
Morphological Characterization of Some Besni Pepper (Capsicum annuum L.) Genotypes in Kayseri Conditions

The pepper genetic resources, which is a widely produced and consumed vegetable in Turkey and the world, are faced with some threats arising from the environmental conditions and agricultural activities. Therefore, it is very important to protect pepper genetic resources and include them in breeding programs. During the production adventure of pepper in Turkey, pepper genotypes known by the name of the regions were developed in different regions such as Demre, Uşak, Karaisalı, and Arapkir pepper. One of them, Besni Pepper, is grown in and around Besni district of Adıyaman province and makes significant contributions to the regional economy. In this study, 26 pepper genotypes collected from the villages of Besni and Gölbaşı districts, and three control varieties were characterized according to 42 morphological traits. Pepper genotypes showed significant variation in terms of the characteristics considered. Principal Component Analysis (PCA) was applied to the investigated traits. The PCA analysis yielded 10 principal components explaining 86% of the total variation. The eigen values of 10 PC’s varied from 10,50 to 1,10. The first three PC’s explain 51,20% of the total variance. The variation between genotypes is mostly due to fruit characteristics such as fruit shape, fruit size, and blossom end shape. While the genotypes were divided into 4 groups in the cluster analysis, the pepper varieties used as control were separated from all genotypes and formed a separate group.
Deep Reinforcement Learning for Detection of Abnormal Anatomies

Automatic detection of abnormal anatomies or malformations of different structures of the human body is a challenging task that could provide support for clinicians in their daily practice. Compared to normative anatomies, there is a low presence of anatomical abnormalities in patients, and the great variation within malformations make it challenging to design deep learning frameworks for automatic detection. We propose a framework for anatomical abnormality detection, which benefits from using a deep reinforcement learning model for landmark detection trained in normative data. We detect the abnormalities using the variability between the predicted landmarks configurations in a subspace based on a point distribution model of landmarks using Procrustes shape alignment and principal component analysis projection from normative data. We demonstrate the performance of this implementation on clinical CT scans of the inner ear, and show how synthetically created abnormal cochlea anatomy can be detected using the prediction of five landmarks around the cochlea. Our approach shows a Receiver Operating Characteristics (ROC) Area Under The Curve (AUC) of 0.97, and 96% accuracy for the detection of abnormal anatomy on synthetic data.