Miracle-dz/UniFAR-FTUs huggingface dataset:
Lookahead Placement Optimization with Cell Library-based Pin Accessibility Prediction via Active Learning

With the development of advanced process nodes of semiconductor, the problem of pin access has become one of the major factors to impact the occurrences of design rule violations (DRVs) due to complex design rules and limited routing resource. Many state-of-the-art works address the problem of DRV prediction by adopting supervised machine learning approaches. However, those supervised learning approaches extract the labels of training data by generating a great number of routed designs in advance, giving rise to large effort on training data preparation. In addition, the pre-trained model could hardly predict unseen data and thus may not be applied to predict other designs containing cells that are not used in the training data. In this paper, we propose the first work of cell library-based pin accessibility prediction (PAP) by using active learning techniques. A given set of standard cell libraries is served as the only input for model training. Unlike most of existing studies that aim at design-specific training, we propose a library-based model which can be applied to all designs referencing to the same standard cell library set. Experimental results show that the proposed model can be applied to predict two different designs with different reference library sets. The number of remaining DRVs and M2 shorts of the designs optimized by the proposed model are also much fewer than those of design-specific models.
Extracting medical entities from social media

Accurately extracting medical entities from social media is challenging because people use informal language with different expressions for the same concept, and they also make spelling mistakes. Previous work either focused on specific diseases (e.g., depression) or drugs (e.g., opioids) or, if working with a wide-set of medical entities, only tackled individual and small-scale benchmark datasets (e.g., AskaPatient). In this work, we first demonstrated how to accurately extract a wide variety of medical entities such as symptoms, diseases, and drug names on three benchmark datasets from varied social media sources, and then also validated this approach on a large-scale Reddit dataset. We first implemented a deep-learning method using contextual embeddings that upon two existing benchmark datasets, one containing annotated AskaPatient posts (CADEC) and the other containing annotated tweets (Micromed), outperformed existing state-of-the-art methods. Second, we created an additional benchmark dataset by annotating medical entities in 2K Reddit posts (made publicly available under the name of MedRed) and showed that our method also performs well on this new dataset. Finally, to demonstrate that our method accurately extracts a wide variety of medical entities on a large scale, we applied the model pre-trained on MedRed to half a million Reddit posts. The posts came from disease-specific subreddits so we could categorise them into 18 diseases based on the subreddit. We then trained a machine-learning classifier to predict the post's category solely from the extracted medical entities. The average F1 score across categories was . 87. These results open up new cost-effective opportunities for modeling, tracking and even predicting health behavior at scale.
Design Optimization by Fine-grained Interleaving of Local Netlist Transformations in Lagrangian Relaxation

Design optimization modifies a netlist with the goal of satisfying the timing constraints at the minimum area and leakage power, without violating any slew or load capacitance constraints. Lagrangian relaxation (LR) based optimization has been established as a viable approach for this. We extend LR-based optimization by interleaving in each iteration techniques such as: gate and flip-flop sizing; buffering to fix late and early timing violations; pin swapping; and useful clock skew. Locally optimal decisions are made using LR-based cost functions, without the need for incremental timing updates. Sub-steps are applied in a balanced manner, accounting for the expected savings and any conflicting timing violations, maximizing the final quality of results under multiple process/operating corners with a reasonable runtime. Experimental results show that our approach achieves better timing, and both lower area and leakage power than the winner of the TAU 2019 contest, on those benchmarks.
ISPD 2020 Physical Mapping of Neural Networks on a Wafer-Scale Deep Learning Accelerator

This paper introduces a special case of the floorplanning problem for optimizing neural networks to run on a wafer-scale computing engine. From a compute perspective, neural networks can be represented by a deeply layered structure of compute kernels. During the training of a neural network, gradient descent is used to determine the weight factors. Each layer then uses a local weight tensor to transform "activations" and "gradients" that are shared among connected kernels according to the topology of the network. This process is computationally intensive and requires high memory and communication bandwidth. Cerebras has developed a novel computer system designed for this work that is powered by a 21.5cm by 21.5cm wafer-scale processor with 400,000 programmable compute cores. It is structured as a regular array of 633 by 633 processing elements, each with its own local high bandwidth SRAM memory and direct high bandwidth connection to its neighboring cores. In addition to supporting traditional execution models for neural network training and inference, this engine has a unique capability to compile and compute every layer of a complete neural network simultaneously. Mapping a neural network in this fashion onto Cerebras' Wafer-Scale Engine (WSE) is reminiscent of the traditional floorplanning problem in physical design. A kernel ends up as a rectangle of x by y compute elements. These are the flexible blocks that need to be placed to optimize performance. This paper describes an ISPD 2020 challenge to develop algorithms and heuristics that produce compiled neural networks that achieve the highest possible performance on the Cerebras WSE.
Retrospective Analysis of Fractures and Factors Causing Ambulation Loss After Lower Limb Fractures in Duchenne Muscular Dystrophy

Objective Prevalence and characteristics of fractures and factors related to loss of ambulation after lower limb fractures were investigated. Design Chart review included height, weight, dual-energy x-ray absorptiometry, corticosteroid use, vitamin D, fracture history, muscle strength, range of motion, and timed performance tests (10 meter walk/run, Gowers, and four steps). Patients were grouped by fracture location and ambulation loss after fracture. Results Two hundred eighty-seven patients with Duchenne muscular dystrophy were identified, 53 of these had experienced fracture. Eighty-one percent were older than 9 yrs at first fracture and 36.4% became nonambulatory after fracture. Dorsiflexion range of motion (fracture side, P = 0.021), quadriceps strength (right side, P = 0.025), and shoulder abduction strength (right, left, and fracture side; P = 0.028, P = 0.027, and P = 0.016) were significantly different within the groups. Patients who became nonambulatory after fracture initially had less dorsiflexion (right, left, fracture side; 2.250 vs. −7.290, P = 0.004; 2.670 vs. −120, P = 0.001; and 2.410 vs. −7.420, P = 0.002) and slower 10-meter walk/run times (7.43 secs vs. 14.7 secs, P = 0.005). Conclusions Fracture represents a significant risk in patients with Duchenne muscular dystrophy; both slower walking speed and ankle contracture confer an increased risk of ambulation loss after fracture. To Claim CME Credits Complete the self-assessment activity and evaluation online at http://www.physiatry. org/JournalCME CME Objectives Upon completion of this article, the reader should be able to: (1) Identify the main factors that are associated with ambulation loss after fracture in patients with Duchenne muscular dystrophy; (2) Identify the risk of fracture in the Duchenne muscular dystrophy population; and (3) Articulate the characteristics associated with fracture in patients with Duchenne muscular dystrophy. Level Advanced. Accreditation The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.
Microbiota Alters Urinary Bladder Weight and Gene Expression

We studied the effect of microbiota on the transcriptome and weight of the urinary bladder by comparing germ-free (GF) and specific pathogen-free (SPF) housed mice. In total, 97 genes were differently expressed (fold change > ±2; false discovery rate (FDR) p-value < 0.01) between the groups, including genes regulating circadian rhythm (Per1, Per2 and Per3), extracellular matrix (Spo1, Spon2), and neuromuscular synaptic transmission (Slc18a3, Slc5a7, Chrnb4, Chrna3, Snap25). The highest increase in expression was observed for immunoglobulin genes (Igkv1-122, Igkv4-68) of unknown function, but surprisingly the absence of microbiota did not change the expression of the genes responsible for recognizing microbes and their products. We found that urinary bladder weight was approximately 25% lighter in GF mice (p = 0.09 for males, p = 0.005 for females) and in mice treated with broad spectrum of antibiotics (p = 0.0002). In conclusion, our data indicate that microbiota is an important determinant of urinary bladder physiology controlling its gene expression and size.
Stepwise progressive parametric multiple testing procedure with correlated normal test statistics

ABSTRACT Multiple testing problems are often seen in clinical trials. An appropriate testing procedure should be specified to deal with the potential inflation of type I error rate due to multiplicity. In this article, we propose a stepwise progressive parametric multiple (SPPM) testing procedure, which constructs the testing using the products of all the combinations of local -values and the critical values are determined by numerical integrations progressively using the closure principle. We have compared the performance of SPPM to several other procedures, and demonstrate the advantage of SPPM procedure, in terms of power, for the certain situations of multiple testing.
Altered Expression of the Epithelial Mucin MUC1 Accompanies Endoscopic Recurrence of Postoperative Crohn’s Disease

Background: MUC1-glycoprotein is expressed at low levels and in fully glycosylated form on epithelial cells. Inflammation causes MUC1 overexpression and hypoglycosylation. We hypothesized that overexpression of hypoglycosylated MUC1 would be found in postoperative Crohn’s disease (CD) recurrence and could be considered an additional biomarker of recurrence severity. Methods: We examined archived neo-terminal ileum biopsies from patients with prior ileocecal resection who had postoperative endoscopic assessment of CD recurrence and given a Rutgeerts ileal recurrence score. Consecutive tissue sections were stained using 2 different anti-MUC1 antibodies, HMPV that recognizes all forms of MUC1 and 4H5 that recognizes only inflammation-associated hypoglycosylated MUC1. Results: A total of 71 postoperative CD patients were evaluated. There was significant increase in MUC1 expression of both glycosylated/normal (P<0.0001) and hypoglycosylated/abnormal (P<0.0001) forms in patients with severe endoscopic CD recurrence (i3+i4), ileal score i2, compared with patients in endoscopic remission (i0+i1). Results were similar regardless of anti-TNF-α use. Although MUC1 expression and Rutgeerts scores were in agreement when characterizing the majority of cases, there were a few exceptions where MUC1 expression was characteristic of more severe recurrence than implied by Rutgeerts score. Conclusions: MUC1 is overexpressed and hypoglycosylated in neo-terminal ileum tissue of patients with postoperative CD recurrence. Increased levels are associated with more severe endoscopic recurrence scores, and this is not influenced by anti-TNF-α use. Discrepancies found between Rutgeerts scores and MUC1 expression suggest that addition of MUC1 as a biomarker of severity of postoperative CD recurrence may improve categorization of recurrence status and consequently treatment decisions.
Maximising trichiasis surgery success (MTSS) trial: rationale and design of a randomised controlled trial to improve trachomatous trichiasis surgical outcomes

Introduction Trachomatous trichiasis (TT) is a condition in which the eyelid turns inward and eyelashes abrade the front part of the eye. To prevent eventual blindness, surgery is recommended. Two surgical procedures are commonly used, bilamellar tarsal rotation (BLTR) and posterior lamellar tarsal rotation (PLTR). Evidence suggests that incision height and surgery type may affect the risk of postoperative TT (PTT) and other surgical outcomes. However, these studies have not prospectively compared the impact of incision height on surgical outcomes. Methods and analysis Maximising trichiasis surgery Success (MTSS) is a three-arm, randomised clinical trial being conducted in Ethiopia. Participants will be randomly assigned on a 1:1:1 basis to BLTR with a 3 mm incision height, BLTR with a 5 mm incision height, or PLTR 3 mm incision height. Patients are eligible for the trial if they have previously unoperated upper eyelid TT. Follow-up visits will be conducted by trained eye examiners at 1 day, 2 weeks, 6 weeks and 12 months after surgery. The primary outcome is incident PTT within 1 year following surgery. Logistic regression will be used in an intention-to-treat analysis to assess outcome incidence by surgical approach. Ethics and dissemination The University of North Carolina and Johns Hopkins School of Medicine institution review boards, Ethiopian National Research Ethics Review Committee and Ethiopian Food, Medicine, Healthcare and Administration and Control Authority provided ethics approval for the trial. On completion, trial results will be disseminated at local and international meetings and in peer-reviewed journals. Trial registration number NCT03100747.
Bayesian inference of chromatin structure ensembles from population-averaged contact data

Significance High-throughput chromosome conformation capture (5C, Hi-C) experiments deliver fascinating insights into 3D genome organization. They are typically performed on a large population of cells and therefore yield only average numbers of genomic contacts. Nevertheless, population Hi-C data are often interpreted in terms of a single genomic structure, which ignores all cell-to-cell variability. We propose a probabilistic, statistically rigorous method to infer chromatin structure ensembles from population Hi-C and similar data that takes the ensemble nature of the data explicitly into account and allows us to infer the number of structures required to explain the data. Mounting experimental evidence suggests a role for the spatial organization of chromatin in crucial processes of the cell nucleus such as transcription regulation. Chromosome conformation capture techniques allow us to characterize chromatin structure by mapping contacts between chromosomal loci on a genome-wide scale. The most widespread modality is to measure contact frequencies averaged over a population of cells. Single-cell variants exist, but suffer from low contact numbers and have not yet gained the same resolution as population methods. While intriguing biological insights have already been garnered from ensemble-averaged data, information about three-dimensional (3D) genome organization in the underlying individual cells remains largely obscured because the contact maps show only an average over a huge population of cells. Moreover, computational methods for structure modeling of chromatin have mostly focused on fitting a single consensus structure, thereby ignoring any cell-to-cell variability in the model itself. Here, we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the optimal number of states in a principled, objective way. We illustrate our approach on simulated data and compute multistate models of chromatin from chromosome conformation capture carbon copy (5C) data. Comparison with independent data suggests that the inferred ensembles represent the underlying sample population faithfully. Harnessing the rich information contained in multistate models, we investigate cell-to-cell variability of chromatin organization into topologically associating domains, thus highlighting the ability of our approach to deliver insights into chromatin organization of great biological relevance.
Gut microbiota and pro/prebiotics in Alzheimer’s disease

Alzheimer’s disease is characterized by the accumulation of amyloid and dysfunctional tau protein in the brain along with the final development of dementia. Accumulation of amyloid in the brain was observed 10-20 years before the onset of clinical symptoms by diagnostic methods based on image analysis. This is a serious public health problem, incidence and prevalence being expected to reach epidemic proportions over the next few decades if the disease cannot be prevented or slowed down. Recently, in addition to the strongly developing ischemic etiology of Alzheimer’s disease, it is suggested that the gut microbiota may also participate in the development of this disease. The brain and gut are thought to form a network called the “gut-brain-microbiota axis”, and it is strongly supported idea that the intestinal microflora can be involved in Alzheimer’s disease. Lately, many new studies have been conducted that draw attention to the relationship between Alzheimer’s disease and gut microbiota. This review presents a possible relationship between Alzheimer’s disease and a microbiome. It is a promising idea for prevention or therapeutic intervention. Modulation of the gut microbiota through a personalized diet or beneficial microflora intervention like pro/prebiotics, changing microbiological partners and their products, including amyloid protein, can become a new treatment for Alzheimer’s disease.
Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals

Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (±0.14) and 0.43 (±0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.
PPARs as Metabolic Regulators in the Liver: Lessons from Liver-Specific PPAR-Null Mice

Peroxisome proliferator-activated receptor (PPAR) α, β/δ, and γ modulate lipid homeostasis. PPARα regulates lipid metabolism in the liver, the organ that largely controls whole-body nutrient/energy homeostasis, and its abnormalities may lead to hepatic steatosis, steatohepatitis, steatofibrosis, and liver cancer. PPARβ/δ promotes fatty acid β-oxidation largely in extrahepatic organs, and PPARγ stores triacylglycerol in adipocytes. Investigations using liver-specific PPAR-disrupted mice have revealed major but distinct contributions of the three PPARs in the liver. This review summarizes the findings of liver-specific PPAR-null mice and discusses the role of PPARs in the liver.
Neural differences between chromatic- and luminance-driven attentional salience in visual search

Previous electroencephalographic research on attentional salience did not fully capture the complexities of low-level vision, which relies on both cone-opponent chromatic and cone-additive luminance mechanisms. We systematically varied color and luminance contrast using a visual search task for a higher contrast target to assess the degree to which the salience-computing attentional mechanisms are constrained by low-level visual inputs. In our first experiment, stimuli were defined by contrast that isolated chromatic or luminance mechanisms. In our second experiment, targets were defined by contrasts that isolated or combined achromatic and chromatic mechanisms. In both experiments, event-related potential waveforms contralateral and ipsilateral to the target were qualitatively different for chromatic- compared to luminance-defined stimuli. The same was true of the difference waves computed from these waveforms, with isoluminant stimuli eliciting a mid-latency posterior contralateral negativity (PCN) component and achromatic stimuli eliciting a complex of multiple components, including an early posterior contralateral positivity followed by a late-latency PCN. Combining color with luminance resulted in waveform and difference wave patterns equivalent to those of achromatic stimuli. When large levels of chromaticity contrast were added to targets with small levels of luminance contrast, PCN latency was speeded. In conclusion, the mechanisms underlying attentional salience are constrained by the low-level inputs they receive. Furthermore, speeded PCN latencies for stimuli that combine color and luminance signals compared to stimuli that contain luminance alone demonstrate that color and luminance channels are integrated during pre-attentive visual processing, before top-down allocation of attention is triggered.
Effect of Coffee Cascara Dietary Fiber on the Physicochemical, Nutritional and Sensory Properties of a Gluten-Free Bread Formulation

This study aimed to assess the physicochemical, nutritional and sensory properties of gluten-free breads containing isolated coffee cascara dietary fiber (ICCDF) as a food ingredient. ICCDF was obtained by aqueous extraction. The oil and water holding capacity and the nutritional profile of the novel ingredient were determined. Its safety was certificated by analysis of ochratoxin A, caffeine and gluten. Gluten-free bread formulations were prepared enriching a commercial bakery premix in rice protein (8%) and ICCDF (3% and 4.5%). Nutritional profile of the novel gluten-free breads (dietary fiber, protein, amino acids, lipids, fatty acid profile and resistant starch), as well as bread volume, crumb density, moisture, firmness, elasticity and color intensity were determined. A sensory quantitative descriptive analysis of the breads was conducted using eight trained panelists. New breads showed significantly higher (p < 0.05) content of dietary fiber and protein than the control bread. The addition of ICCDF allowed increasing dough yield, a less crumb firmness and a higher crumb elasticity. The nutrition claims “source of protein and high in dietary fiber” were assigned to the new formulations. In conclusion, a certificated gluten-free bread with improved nutritional and physicochemical properties and good sensorial profile was obtained.