Miracle-dz/UniFAR-FTUs huggingface dataset:
Contact guidance is cell cycle-dependent

Cancer cell migration is essential for metastasis, during which cancer cells move through the tumor and reach the blood vessels. In vivo, cancer cells are exposed to contact guidance and chemotactic cues. Depending on the strength of such cues, cells will migrate in a random or directed manner. While similar cues may also stimulate cell proliferation, it is not clear whether cell cycle progression affects migration of cancer cells and whether this effect is different in random versus directed migration. In this study, we tested the effect of cell cycle progression on contact guided migration in 2D and 3D environments, in the breast carcinoma cell line, FUCCI-MDA-MB-231. The results were quantified from live cell microscopy images using the open source lineage editing and validation image analysis tools (LEVER). In 2D, cells were placed inside 10 μm-wide microchannels to stimulate contact guidance, with or without an additional chemotactic gradient of the soluble epidermal growth factor. In 3D, contact guidance was modeled by aligned collagen fibers. In both 2D and 3D, contact guidance was cell cycle-dependent, while the addition of the chemoattractant gradient in 2D increased cell velocity and persistence in directionally migrating cells, regardless of their cell cycle phases. In both 2D and 3D contact guidance, cells in the G1 phase of the cell cycle outperformed cells in the S/G2 phase in terms of migration persistence and instantaneous velocity. These data suggest that in the presence of contact guidance cues in vivo, breast carcinoma cells in the G1 phase of the cell cycle may be more efficient in reaching the neighboring vasculature.
Preterm infant gut microbial patterns related to the development of necrotizing enterocolitis

Abstract Objectives: To define gut microbial patterns in preterm infants with and without necrotizing enterocolitis (NEC) and to characterize clinical factors related to the composition of the preterm intestinal microbiome. Methods: Fecal samples were collected at one-week intervals from infants with gestational ages <30 weeks at a single level IV neonatal intensive care unit. Using 16S rRNA gene sequencing, the composition and diversity of microbiota were determined in samples collected from five NEC infants and five matched controls. Hierarchical linear regression was used to identify clinical factors related to microbial diversity and specific bacterial signatures. Results: Low levels of diversity were demonstrated in samples obtained from all preterm infants and antibiotic exposure further decreased diversity among both NEC cases and controls. Fecal microbial composition differed between NEC cases and controls, with a greater abundance of Proteobacteria and bacteria belonging to the class Gammaproteobacteria among NEC infants. Control infants demonstrated a greater abundance of bacteria belonging to the phylum Firmicutes. Conclusion: These findings indicate that an association exists between intestinal Proteobacteria and NEC, and strengthens the notion that an overly exuberant response to Gram-negative products, particularly lipopolysaccharide, in the preterm intestine is involved in NEC pathogenesis. Cumulative exposure to antibiotics corresponded to a reduction in microbial diversity in both NEC cases and controls.
Delayed Treatment Acceleration in Patients with Rheumatoid Arthritis Who Have Inadequate Response to Initial Tumor Necrosis Factor Inhibitors: Data from the Corrona Registry.

Background The implementation of treat-to-target principles in rheumatoid arthritis (RA) has not been fully investigated in patients with inadequate response to tumor necrosis factor (TNF) inhibitor treatment. Objectives To evaluate the prevalence of an inadequate response to initial TNF inhibitor treatment at 6 and 12 months among patients with RA in a real-world patient registry, as well as the delay in therapy adjustment and its impact on disease activity and patient-reported outcome (PRO) measures. Methods This analysis is based on data of patients with moderate or severe disease activity (Clinical Disease Activity Index [CDAI] score >10) who were included in the Consortium of Rheumatology Researchers of North America (Corrona) RA registry, a prospective, observational database. The patients had never received treatment with a biologic disease-modifying antirheumatic drug (DMARD) and had initiated treatment with a TNF inhibitor (adalimumab, certolizumab pegol, etanercept, golimumab, or infliximab) between October 2001 and December 2014. We evaluated treatment response (CDAI score ≤10), select PRO measures, and treatment changes at 6 months. Patients who had an inadequate response to TNF inhibitor therapy at 6 months and continued to use their initial TNF inhibitor were evaluated again at 12 months. Results This retrospective analysis included 2282 patients. At 6 months, 1732 (75.9%) of the patients continued to use their initial TNF inhibitor; of these, 803 (46.4%) patients had an inadequate response to treatment. Of the 803 patients who had an inadequate response at 6 months, 488 (60.8%) continued their initial treatment at 12 months. Of these 488 patients, 315 (64.5%) had an inadequate response at 12 months, and 173 (35.5%) had a response. Numerically greater improvements in all PRO measures were observed for patients who responded to therapy compared with patients with an inadequate response. Conclusions In this real-world analysis of data from the Corrona RA registry, a considerable proportion of patients with RA had an inadequate response to the initial TNF inhibitor therapy at 6 and 12 months. Many patients continued to have moderate or high disease activity, without accelerating treatment (eg, addition or increase in the dose of concurrent conventional synthetic DMARDs or a TNF inhibitor), contrary to treat-to-target principles, thus remaining at risk for accumulating joint damage and disability.
Poverty and Climate Change in Urban Bangladesh (Climurb): An Analytical Framework

Around 40 percent of Bangladesh’s population are poor people for whom a variable and unpredictable climate can critically restrict livelihood options. This is true in rural and urban areas alike, but this study focuses on the latter. Urban poverty continues to be neglected in research, policy and action for climate change adaptation in the country. The study builds on three propositions: (i) poor urban communities are places where physical and socioeconomic vulnerability coincide; (ii) urban areas are exposed to three forms of climate change impact: rapid-onset events, gradual-onset processes, and cascade effects; and (iii) poor urban people are already adapting to emergent climate change impacts by actively developing various practices. The analytical framework places a strong emphasis on poor people’s adaptation practices in order to understand their agency, cultural resources and economic strategies and the structural factors that both support and constrain their agency. The practices are examined in terms of three key elements: the socio-economic resources of poor urban households and communities; institutions and political economy; and external actors and resources. Six low-income settlements have been chosen for case studies from three cities – Dhaka, Chittagong and Khulna. Data collection involves: mini-surveys; qualitative methods; dialogues with local academics, policymakers and civil society groups; and action research. Key analytical findings include the identification and analysis of existing practices under five broad themes (e.g. livelihoods, built environment, networks, institutions, and external supports).
Design of an energy regenerative system for a robotic leg prosthesis

The number of lower limb amputations have significantly increased over the years and this calls for improvement in the technology of prosthetic devices. Compared to the rudimentary passively operated devices, the latest active prostheses require electrical energy supplied from the battery for various tasks like operating motors, clutches and microprocessors. The objective of this study is to increase the energy efficiency of the device by employing a mechanism which will help regenerate energy so that the system's dependence on an external battery is reduced. A crank slider mechanism is appended to the prosthetic leg and the linear motion of the slider is used to harness energy using the principle of electromagnetic induction. A permanent magnet is included in the slider which moves interior to a coil and generates a voltage. The mechanism will be actuated due to the motion of the knee motor. The supercapacitor technology is used to store and supply this energy at regular intervals. A dynamic simulation is carried out in MATLAB to investigate the energy regenerated in the prosthetic device while the amputee walks, runs, sprints or climbs the stairs up and down. Single and multiple coil models are designed and tested for best performance statistics. Also, extra energy spent by the motor to carry the weight of the mechanism and to overcome friction is computed. Analysis of all the above factors for various human motions will verify the usefulness of the proposed system and this will mark an establishment of an electrically regenerative robotic leg prosthesis.
Unsupervised POS Tagging of Low Resource Language for Machine Translation

In this paper, we attempt to increase statistical machine translation (SMT) performance for a low resource language, Myanmar, by applying POS (part-of-speech) tags induced with a novel bilingual infinite HMM approach. Like in other low resource languages, POS tagged corpora for Myanmar are not yet available and thus we considered learning POS tags from an existing un-tagged inhouse corpus. A number of unsupervised methods have been proposed for inducing POS tags including nonparametric Bayesian methods that automatically select the number of POS tags. (Gael et al., 2009) applied infinite HMM (iHMM) (Beal et al., 2001; Teh et al., 2006), a nonparametric version of an HMM, to POS tag induction. (Tamura et al., 2013) proposed a non-parametric Bayesian method for inducing POS tags from dependency trees to improve the performance of SMT. In case of Myanmar, there is no dependency tree data nor dependency parser yet and thus we extended iHMM approach for bilingual POS tags. The experimental results show that phrase based SMT with words that were tagged with induced POS tags gains over 2 points in BLEU for Myanmar to English translation.
Chapter 2 The Bone Marrow Microenvironment for Hematopoietic Stem Cells

The main function of the microenvironment in the bone marrow (BM) is to provide signals that regulate and support the production of the billions of blood cells necessary to maintain homeostasis. The best characterized BM microenvironment is the niche that regulates hematopoietic stem cells. Efforts from many different laboratories have revealed that the niche is mainly perivascular and that blood vessels and perivascular stromal cells are the key components. In addition numerous cell types have been shown to be components of the niche. Here we discuss our current understanding of the niche and the evidence supporting the role of different types of cells in regulating hematopoietic stem cell numbers and function in vivo.
Skeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks

In this paper, we present an automated skeletal maturity recognition system that takes a single hand radiograph as an input and finally output the bone age prediction. Unlike the conventional manually diagnostic methods, which are laborious, fallible, and time-consuming, the proposed system takes input images and generates classification results directly. It first accurately detects the distal radius and ulna areas from the hand and wrist X-ray images by a faster region-based convolutional neural network (CNN) model. Then, a well-tuned CNN classification model is applied to estimate the bone ages. In the experiment section, we employed a data set of 1101 hand and wrist radiographs and conducted comprehensive experiments on the proposed system. We discussed the model performance according to various network configurations, multiple optimization algorithms, and different training sample amounts. After parameter optimization, the proposed model is finally achieved 92% and 90% classification accuracies for radius and ulna grades, respectively.
An improved collaborative filtering recommendation algorithm based on reliability

The similarity of the user-based collaborative filtering algorithm is based on the user-item rating matrix. However, the accuracy of predicting the users' rating was affected by the data sparseness problem. To alleviate the data sparseness problem of the collaborative filtering and the variant ratings on the common items. In this paper, we propose a similarity calculation measure that based on reliability. Firstly, the measure makes use of the users' ratings on the common items to obtain the credibility of rating between users, and then introduces the credibility into the adjusted cosine similarity to alleviate the affect of variant ratings on common items. Secondly, the punishment function was introduced to the adjusted cosine similarity, and alleviate the influence of the popular items on the similarity calculation. Finally, the former two kinds of similarity were measured comprehensively to predict the user's rating more accurate and to improve the reliability of the similarity calculation. Experimental results show that compared with the Pearson similarity and the adjusted cosine similarity, the improved method proposed in this paper could get a lower value of MAE, which means that it could improve the accuracy of predicting users' rating and the personalized recommendation efficiency.
Differential Bilateral Primary Motor Cortex tDCS Fails to Modulate Choice Bias and Readiness in Perceptual Decision Making

One of the critical factors that guide choice behavior is the prior bias of the decision-maker with respect to different options, namely, the relative readiness by which the decision-maker opts for a specific choice. Although previous neuroimaging work has shown decision bias related activity in the orbitofrontal cortex, intraparietal sulcus (IPS) and dorsolateral prefrontal cortex, in a recent work by Javadi et al. (2015), primary motor cortex was also implicated. By applying transcranial direct current stimulation (tDCS), they have revealed a causal role of the primary motor cortex excitability in the induction of response time (RT) differences and decision bias in the form of choice probability. The current study aimed to replicate these recent findings with an experimental design that contained a sham group to increase experimental control and an additional testing phase to investigate the possible after-effects of tDCS. The conventional decision outputs such as choice proportion and RT were analyzed along with the theory-driven estimates of choice bias and non-decision related components of RTs (e.g., motor implementation speed of choices made). None of the statistical comparisons favored the alternative hypotheses over the null hypotheses. Consequently, previous findings regarding the effect of primary motor cortex excitability on choice bias and response times could not be replicated with a more controlled experimental design that is recommended for tDCS studies (Horvath et al., 2015). This empirical discrepancy between the two studies adds to the evidence demonstrating inconsistent effects of tDCS in establishing causal relationships between cortical excitability and motor behavior.
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-the-art DNNs on current systems mostly relies on either generalpurpose processors, ASIC designs, or FPGA accelerators, all of which suffer from data movements due to the limited onchip memory and data transfer bandwidth. In this work, we propose a novel framework, called RAPIDNN, which processes all DNN operations within the memory to minimize the cost of data movement. To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i.e., multiplication, addition, activation functions, and pooling. The framework extracts representative operands of a DNN model, e.g., weights and input values, using clustering methods to optimize the model for in-memory processing. Then, it maps the extracted operands and their precomputed results into the accelerator memory blocks. At runtime, the accelerator identifies computation results based on efficient in-memory search capability which also provides tunability of approximation to further improve computation efficiency. Our evaluation shows that RAPIDNN achieves 68.4x, 49.5x energy efficiency improvement and 48.1x, 10.9x speedup as compared to ISAAC and PipeLayer, the state-of-the-art DNN accelerators, while ensuring less than 0.3% of quality loss.
Micro-fabrication method of Au micro-wire structures on substrate by chemical printing and peeling process

In this paper, a newly developed manufacturing process of metallic micro structure arrays on a substrate is reported. This process comprises of chemical treating, sputter coating and peeling techniques. Firstly in this process, a silicon wafer mother mold of micro patterns is prepared by photolithography and dry etching technique and its micro patterns is transferred to a polymer film by hot embossing method. Then, a quartz glass is cleaned in the acetone bath and then by sputter etching before stamping process. A polymer film mold, whose surface has been patterned with the micro line structure arrays, is used to transfer directly of acetone molecular patterns onto the etched surface of quartz glass substrate. Then a substrate is coated with a gold film by the DC sputter coating method. Effect of the remarkable of acetone micro patterned to the adhesion behavior of gold film on a substrate is study experimentally. Finally, peeling technique by the vibration hot dipping technique is study. As a result, the metal films on the acetone stamped lines are removed by vibration hot dipping process, and micro-wire structure arrays are remained on the substrate. This is because of chemical stamp is effective to reduce surface energy of the substrate, and also reduce the adhesion between the Au film and substrate. Feasibility of the proposed process is confirmed by experimental study, and efficiency of the process is discussed.
An Analytical Investigation on the Charge Distribution and Gate Control in the Normally-Off GaN Double-Channel MOS-HEMT

A systematic analytical investigation of the charge distribution and gate control of the normally-off GaN double-channel MOS-HEMT (DC-MOS-HEMT) is presented in this paper. Compared to conventional GaN MOS-HEMT, the DC-MOS-HEMT features a thin AlN insertion layer (AlN-ISL) below the original two dimensional electron gas (2DEG) channel, thus forming a second channel at the interface between AlN-ISL and the underlying GaN. This paper reveals the impact of the AlN-ISL on the 2DEG distribution and the gate control of the channels. The sensitivity of ${V}_{\text {th}}$ against the recess depth is also analytically studied and is found to be nearly independent of the recess depth as long as the recess is terminated in the upper channel layer. The analytical results are well supported by numerical device simulations, and the physical mechanisms behind these findings are explained along with the analytical investigations.
The transformation of the business angel market: empirical evidence and research implications

Abstract Business angel investing – a key source of finance for entrepreneurial businesses – is rapidly evolving from a fragmented and largely anonymous activity dominated by individuals investing on their own to one that is increasingly characterised by groups of investors investing together through managed angel groups. The implications of this change have been largely ignored by scholars. The paper examines the investment activity and operation of angel groups in Scotland to highlight the implications of this change for the nature of angel investing. It goes on to argue that this transformation challenges both the ongoing relevance of prior research on business angels and current methodological practices, and raises a set of new research questions.
Efficiency and Power Limits of Electrical and Tendon-Sheath Transmissions for Surgical Robotics

A popular design choice in current surgical robotics is to use mechanical cables to transmit mechanical energy from actuators located outside of the body, through a minimally invasive port, to instruments on the inside of the body. These cables enable high performance surgical manipulations including high bandwidth control, precision position control, and high force ability. However, cable drives become less efficient for longer distances, for paths that involve continuous curves, and for transmissions involving multiple degrees of freedom. In this paper, we consider the design tradeoffs for two methods of transmitting power through an access port with limited cross sectional area and curved paths - tendon/sheath mechanical transmissions and electrical wire transmissions. We develop a series of analytic models examining fundamental limits of efficiency, force and power as constrained by access geometry, material properties, and safety limits of heat and electrical hazards for these two transmission types. These models are used to investigate the potential of achieving the required mechanical power requirements needed for surgery with smaller access ports and more difficult access pathways. We show that an electrical transmission is a viable way of delivering more than sufficient power needed for surgery, highlighting the opportunity for next-generation actuators to enable more minimally invasive surgical devices.
Hybrid Electric Vehicle Downshifting Strategy Based on Stochastic Dynamic Programming During Regenerative Braking Process

Regenerative braking is the most important way to save energy for hybrid electric vehicles (HEVs) and electric vehicles (EVs). Wherein, for HEVs/EVs equipped with stepped automatic transmissions, downshifting is an effective method to improve recovery energy by adjusting motor work points. However, it is difficult to design a proper downshifting strategy due to complicated traffic conditions and unknown drivers’ intention. Therefore, this paper proposes a downshifting strategy using cluster-based stochastic dynamic programing (SDP). First, driving conditions are clustered using K-means algorithm based on a large quantity of urban traffic historical data. Then, static Markov chains are built to describe the transition of the future braking torque demand for each cluster. Next, a four-dimensional SDP optimization problem for downshifting is formulated and solved through Bellman iteration. Finally, support vector machine is adopted to identify the driving conditions online, based on which SDP results are used to give the downshifting command. Simulations and controller-in-loop tests are carried out, and the results show that regenerative braking could recover more energy by SDP-based strategy than no downshifting and rule-based strategy.