Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances in Micro-Electro-Mechanical Devices for Controlled Drug Release Applications

In recent years, controlled release of drugs has posed numerous challenges with the aim of optimizing parameters such as the release of the suitable quantity of drugs in the right site at the right time with the least invasiveness and the greatest possible automation. Some of the factors that challenge conventional drug release include long-term treatments, narrow therapeutic windows, complex dosing schedules, combined therapies, individual dosing regimens, and labile active substance administration. In this sense, the emergence of micro-devices that combine mechanical and electrical components, so called micro-electro-mechanical systems (MEMS) can offer solutions to these drawbacks. These devices can be fabricated using biocompatible materials, with great uniformity and reproducibility, similar to integrated circuits. They can be aseptically manufactured and hermetically sealed, while having mobile components that enable physical or analytical functions together with electrical components. In this review we present recent advances in the generation of MEMS drug delivery devices, in which various micro and nanometric structures such as contacts, connections, channels, reservoirs, pumps, valves, needles, and/or membranes can be included in their design and manufacture. Implantable single and multiple reservoir-based and transdermal-based MEMS devices are discussed in terms of fundamental mechanisms, fabrication, performance, and drug release applications.
Utilization of Digital PCR in Quantity Verification of Plasmid Standards Used in Quantitative PCR

Quantitative PCR (qPCR) is a widely used method for nucleic acid quantification of various pathogenic microorganisms. For absolute quantification of microbial load by qPCR, it is essential to create a calibration curve from accurately quantified quantification standards, from which the number of pathogens in a sample is derived. Spectrophotometric measurement of absorbance is a routine method for estimating nucleic acid concentration, however, it may be affected by presence of other potentially contaminating nucleic acids or proteins and salts. Therefore, absorbance measurement is not reliable for estimating the concentration of stock solutions of quantification standards, based on which they are subsequently diluted. In this study, we utilized digital PCR (dPCR) for absolute quantification of qPCR plasmid standards and thus detecting possible discrepancies in the determination of the plasmid DNA number of standards derived from UV spectrophotometry. The concept of dPCR utilization for quantification of standards was applied on 45 qPCR assays using droplet-based and chip-based dPCR platforms. Using dPCR, we found that spectrophotometry overestimated the concentrations of standard stock solutions in the majority of cases. Furthermore, batch-to-batch variation in standard quantity was revealed, as well as quantitative changes in standards over time. Finally, it was demonstrated that droplet-based dPCR is a suitable tool for achieving defined quantity of quantification plasmid standards and ensuring the quantity over time, which is crucial for acquiring homogenous, reproducible and comparable quantitative data by qPCR.
Measuring the Gap Between the Maximum Predictability and Prediction Accuracy of Human Mobility

It has been claimed that human mobility is highly predictable and an upper bound of 93% predictability is achievable. However, there is a significant gap between the upper bound of predictability and the actual prediction accuracy in many data sets. This paper points out that this gap is caused by the difference between the user’s actual distribution and the hypothesis in the derivation through the analysis on the upper bound of predictability. Then two statistics of the target user’s mobility traces are proposed to measure this gap, whose effectiveness is validated by simulated traces and real-world data sets using five prevailing prediction models. The proposed MLP statistics can help with assessing the data quality and designing prediction algorithms. Our work makes the predictability upper bound become a more effective measure and extends the understanding of predictability research in human mobility prediction.
Growth of 'M9' apple root in five Central Europe replanted soils

Soil sickness is a widespread problem in replanted apple orchards with a complex symptomatology and etiology influenced by soil and climate conditions. Consequently, a conclusive technical solution is still lacking for intensive apple orchards. The present work aims to analyze the morphological and functional changes occurring in the M9 apple root systems growing in pot filled with soil derived from five different European growing areas. For each growing area, the soil was collected from the apple orchard and used directly in the pot or gamma-ray sterilized before potting. Soil from a neighborhood fallow was also used as control for each growing area. In the non-sterilized replant soils plants developed poor root systems due to a limited biomass allocation. Fibrous roots production was particularly compromised. The roots had a smaller diameter and a lower ramification index. The root cell membrane integrity was also lower. Gamma-ray sterilized replant soils increased root growth, branching and cell integrity, while nearby fallow soil induced an intermediate root behavior. The magnitude of the symptoms showed a significant interaction between soil treatment and sampling site and root growth was correlated with the organic matter content in the soils.
TWO-PATTERN TEST CAPABILITIES OF AUTONOMOUS LFSR/SR GENERATOR IN PSEUDO-EXHAUSTIVE TESTING

Testing for delay and CMOS stuck-open faults requires two-pattern tests. Built-in self-test (BIST) schemes are required to comprehensive testing of such faults. BIST test pattern generators for two-pattern testing should be designed to ensure high transition coverage. The test pattern generator (TPG) circuits treated here are not limited to linear feedback shift registers (LFSRs) but include autonomous linear feedback shift register / shift register (LFSR/SR) circuits. It is required to increase the number of each subset of the state variables for complete transition coverage with the optimal test lengths.
Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO)

Purpose To develop a rapid and accurate MRI phase unwrapping technique for challenging phase topographies encountered at high magnetic fields, around metal implants or post-operative cavities, that is sufficiently fast to be applied to large group studies including Quantitative Susceptibility Mapping and functional MRI (with phase-based distortion correction). Methods The proposed path-following phase unwrapping algorithm, ROMEO, estimates the coherence of the signal both in space - using MRI magnitude and phase information - and over time, assuming approximately linear temporal phase evolution. This information is combined to form a quality map that guides the unwrapping along a three-dimensional path through the object using a computationally efficient minimum spanning tree algorithm. ROMEO was tested against the two most commonly used exact phase unwrapping methods: PRELUDE and BEST PATH in simulated topographies and at several field strengths: in 3 T and 7 T in vivo human head images and 9.4 T ex vivo rat head images. Results ROMEO was more reliable than PRELUDE and BEST PATH, yielding unwrapping results with excellent temporal stability for multi-echo or multi-time-point data. ROMEO does not require image masking and delivers results within seconds even in large, highly wrapped multi-echo datasets (e.g. 9 seconds for a 7 T head dataset with 31 echoes and a 208 x 208 x 96 matrix size). Conclusion Overall, ROMEO was both faster and more accurate than PRELUDE and BEST PATH delivering exact results within seconds, which is well below typical image acquisition times, enabling potential on-console application.
Adaptive Neural Dynamic Surface Output Feedback Control for Nonlinear Full States Constrained Systems

In order to solve the control problem of uncertain nonlinear systems with state constraints, a dynamic surface output feedback control technology based on Radial Basis Function (RBF) neural networks state observer is proposed. The state observer is designed to estimate the unknown state of the systems by using the approximation characteristics of RBF neural networks, and to constrain the system state by using the Barrier Lyapunov Function (BLF). Based on the backstepping control, a first-order low-pass filter is introduced to design a dynamic surface control (DCS), which solves the “differential explosion” phenomenon that can easily occur in backstepping control. Finally, the stability of the closed-loop system which is confirmed by the Lyapunov method guarantees the semi-globally uniformly ultimately boundedness (SGUUB) of all the signals. The effectiveness of the methods that the boundedness of the tracking errors, the observer states and the controllers can be guaranteed, and good control performance could be achieved is shown by simulation results.
Residual Connection-Based Encoder Decoder Network (RCED-Net) for Retinal Vessel Segmentation

Devising automated procedures for accurate vessel segmentation (retinal) is crucial for timely prognosis of vision-threatening eye diseases. In this paper, a novel supervised deep learning-based approach is proposed which extends a variant of the fully convolutional neural network. The existing fully convolutional neural network-based counterparts have associated critical drawbacks of involving a large number of tunable hyper-parameters and an increased end-to-end training time furnished by their decoder structure. The proposed approach addresses these intricate challenges by using a skip-connections strategy by sharing indices obtained through max-pooling to the decoder from the encoder stage (respective stages) for enhancing the resolution of the feature map. This significantly reduces the number of required tunable hyper-parameters and the computational overhead of the training as well as testing stages. Furthermore, the proposed approach particularly helps in eradicating the requirement for employing both post-processing and pre-processing steps. In the proposed approach, the retinal vessel segmentation problem is formulated as a semantic pixel-wise segmentation task which helps in spanning the gap between semantic segmentation and medical image segmentation. A prime contribution of the proposed approach is the introduction of external skip-connection for passing the preserved low-level semantic edge information in order to reliably detect tiny vessels in the retinal fundus images. The performance of the proposed scheme is analyzed based on the three publicly available notable fundus image datasets, while the widely recognized evaluation metrics of specificity, sensitivity, accuracy, and the Receiver Operating Characteristics curves are used. Based on the assessment of the images in {DRIVE, CHASE_DB1, and STARE}; datasets, the proposed approach achieves a sensitivity, specificity, accuracy, and ROC performance of {0.8252, 0.8440, and 0.8397};, {0.9787, 0.9810, and 0.9792};, {0.9649, 0.9722, and 0.9659};, and {0.9780, 0.9830, and 0.9810};, respectively. The reduced computational complexity and memory overhead along with improved segmentation performance advocates employing the proposed approach in the automated diagnostic systems for eye diseases.
Freshly Thawed Cryobanked Human Neural Stem Cells Engraft within Endogenous Neurogenic Niches and Restore Cognitive Function Following Chronic Traumatic Brain Injury

Human neural stem cells (hNSCs) have potential as a cell therapy following traumatic brain injury (TBI). While various studies have demonstrated the efficacy of NSCs from on-going culture, there is a significant gap in our understanding of freshly thawed cells from cryobanked stocks – a more clinically-relevant source. To address these shortfalls, the therapeutic potential of our previously validated Shef-6.0 human embryonic stem cell (hESC)-derived hNSC line was tested following long-term cryostorage and thawing prior to transplant. Immunodeficient athymic nude rats received a moderate unilateral controlled cortical impact (CCI) injury. At 4-weeks post-injury, 6×105 freshly thawed hNSCs were transplanted into six injection sites (2 ipsi- and 4 contra-lateral) with 53.4% of cells surviving three months post-transplant. Interestingly, most hNSCs were engrafted in the meninges and the lining of lateral ventricles, associated with high CXCR4 expression and a chemotactic response to SDF1alpha (CXCL12). While some expressed markers of neuron, astrocyte, and oligodendrocyte lineages, the majority remained progenitors, identified through doublecortin expression (78.1%). Importantly, transplantation resulted in improved spatial learning and memory in Morris water maze navigation and reduced risk-taking behavior in an elevated plus maze. Investigating potential mechanisms of action, we identified an increase in ipsilateral host hippocampus cornu ammonis (CA) neuron survival, contralateral dentate gyrus (DG) volume and DG neural progenitor morphology as well as a reduction in neuroinflammation. Together, these findings validate the potential of hNSCs to restore function after TBI and demonstrate that long-term bio-banking of cells and thawing aliquots prior to use may be suitable for clinical deployment. Significance Statement There is no cure for chronic traumatic brain injury (TBI). While human neural stem cells (hNSCs) offer a potential treatment, no one has demonstrated efficacy of thawed hNSCs from long-term cryobanked stocks. Frozen aliquots are critical for multisite clinical trials, as this omission impacted the use of MSCs for graft versus host disease. This is the first study to demonstrate the efficacy of thawed hNSCs, while also providing support for novel mechanisms of action – linking meningeal and ventricular engraftment to reduced neuroinflammation and improved hippocampal neurogenesis. Importantly, these changes also led to clinically relevant effects on spatial learning/memory and risk-taking behavior. Together, this new understanding of hNSCs lays a foundation for future work and improved opportunities for patient care.
Using Crop Rotation to Control Meloidogyne hapla Chitwood and Improve Marketable Carrot Yield

The influence of various crop rotations on population densities of Meloidogyne hapla, the northern root-knot nematode, and subsequent carrot yields was studied in organic soil under field conditions. Seven 3-year sequences with barley (Hordeum vulgare L.), carrot (Daucus carota L.), onion (Allium cepa L.), or weedy fallow, all with carrot as the third-year crop, were replicated six times in a completely randomized block design. Carrot monoculture, two seasons of weedy fallow, or carrot followed by onion resulted in high M. hapla population densities and severe root damage on carrot the third year. Barley followed by onion or onion followed by barley harbored low M. hapla population densities and provided the highest yields, with 56.8 and 47.2 t marketable carrots/ha, respectively, compared to 2.2 t.ha -1 in the carrot monoculture. A single crop of barley reduced nematode population densities and provided 88% and 73% marketable carrot roots in the subsequent years. High M. hapla population densities and the high proportion of culls recorded in plots in weed fallow emphasize the importance of an effective weed management program for successfully using crop rotation against root-knot nematode in muck-grown carrot.
Prefetching in Hybrid Main Memory Systems

The architecture of main memory has experienced a paradigm shift in recent years, with non volatile memory technologies (NVM) like Phase Change Memory (PCM) being incorporated into the hierarchy at the same level as DRAM. This transformation is being carried out by either splitting the memory address across two or more memory technologies, or using a faster technology with higher lifetimes, typically the DRAM, as a cache for the higher capacity, albeit slower main memory made up of a NVM. Design of such hybrid architectures remains an active area of research from the perspective of DRAM-as-a-cache design, since DRAM could quickly become the bottleneck, as cache lookups require multiple accesses for reading tag and data. In this paper, we augment the DRAM-as-a-cache model with a novel DRAM cache prefetcher that builds on state of the art Alloy Cache. The new DRAM cache architecture allows for prefetching data at both cacheline and page granularities from the NVM, and as a result, provides upto a maximum of 2 × performance improvement over a state of the art baseline.
Increasing cell-free gene expression yields from linear templates in Escherichia coli and Vibrio natriegens extracts by using DNA-binding proteins

In crude extract-based cell-free protein synthesis (CFPS), DNA templates are transcribed and translated into functional proteins. Although linear expression templates (LETs) are less laborious and expensive to generate, plasmid templates are often desired over PCR-generated LETs due to increased stability and protection against exonucleases present in the extract of the reaction. Here we demonstrate that addition of a dsDNA-binding protein to the CFPS reaction, termed single-chain Cro protein (scCro), achieves terminal protection of LETs. This CroP-LET (scCro-based Protection of LET) method effectively increases sfGFP expression levels from LETs in Escherichia coli CFPS reactions by 6-fold. Our yields are comparable to other strategies that provide chemical and enzymatic DNA stabilization in E. coli CFPS. Notably, we also report that the CroP-LET method successfully enhanced yields in CFPS platforms derived from non-model organisms. Our results show that CroP-LET increased sfGFP yields by 18-fold in the Vibrio natriegens CFPS platform. With the fast-expanding applications of CFPS platforms, this method provides a practical and generalizable solution to protect linear expression DNA templates.
Single-cell Long Non-coding RNA landscape of T cells in human cancer immunity

The development of new therapeutic targets for cancer immunotherapies and the development of new biomarkers require deep understanding of T cells. To date, the complete landscape and systematic characterization of long noncoding RNAs (lncRNAs) in T cells in cancer immunity are lacking. Here, by systematically analyzing full-length single-cell RNA sequencing (scRNA-seq) data of more than 20,000 T cell libraries across three cancer types, we provide the first comprehensive catalog and the functional repertoires of lncRNAs in human T cells. Specifically, we developed a custom pipeline for de novo transcriptome assembly obtaining 9,433 novel lncRNA genes that increased the number of current human lncRNA catalog by 16% and nearly doubled the number of lncRNAs expressed in T cells. We found that a portion of expressed genes in single T cells were lncRNAs which have been overlooked by the majority of previous studies. Based on metacell maps constructed by MetaCell algorithm that partition scRNA-seq datasets into disjointed and homogenous groups of cells (metacells), 154 signature lncRNAs associated with effector, exhausted, and regulatory T cell states are identified, 84 of which are functionally annotated based on co-expression network, indicating that lncRNAs might broadly participate in regulation of T cell functions. Our findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.
Urban Ageing in Europe - Spatiotemporal Analysis of Determinants

The aim of this study was to identify determinants of the population ageing process in 270 European cities. We analyzed the proportion of older people: men and women separately (aged 65 or above) in city populations in the years 1990–2018. To understand territorially-varied relationships and to increase the explained variability of phenomena, an explanatory spatial data analysis (ESDA) and geographically weighted regression (GWR) were applied. We used ArcGIS and GeoDa software in this study. In our research, we also took into account the spatial interactions as well as the structure of cities by size and level of economic development. Results of the analysis helped to explain why some urban areas are ageing faster than others. An initial data analysis indicated that the proportion of the elderly in the population was spatially diversified and dependent on gender, as well as the size and economic development of a unit. In general, elderly individuals were more willing to live in larger and highly developed cities; however, women tended to live in large areas and men in medium-sized to large urban areas. Then, we conducted the urban ageing modelling for men and women separately. The application of GWR models enabled not only the specification of the city population ageing determinants, but also the analysis of the variability in the strength and direction of dependencies occurring between the examined variables in individual cities. Significant differences were noted in the analysis results for specific cities, which were often grouped due to similar parameter values, forming clusters that divided Europe into the eastern and western parts. Moreover, substantial differences in results were obtained for women and men.
How Does Courtroom Broadcasting Influence Public Confidence in Justice? The Mediation Effect of Vicarious Interpersonal Treatment

The present study aimed to examine whether the applied practice of cameras in courtrooms plays a positive role in public confidence in legal authorities and how such impact may occur from the perspectives of the Group Value Model and the surrogacy effect. A convenience sample of 170 college students participated in this experiment. The control group read the written judgment of a civil case published online while the experimental group read the same judgment and watched the court trial video of that case. The overarching mediation model confirmed that there was a significant and indirect influence of video watching on confidence in justice in general. The key underlying mechanisms of this impact were the positive perception of the interpersonal treatment by the judge as well as the perceived fairness of the procedure. This study contributes to the currently limited research examining whether and, if so, how courtroom broadcasting promotes public trust through obtaining empirical evidence. It also expands the application of the Group Value Model to a vicarious interaction setting and provides evidence of the surrogacy effect in a civil legal context.