LLukas22/scidocs huggingface dataset:
Development of Electromagnetic Band Gap Structures in the Perspective of Microstrip Antenna Design

Electromagnetic band gap (EBG) technology has become a significant breakthrough in the radio frequency (RF) and microwave applications due to their unique band gap characteristics at certain frequency ranges. Since 1999, the EBG structures have been investigated for improving performances of numerous RF and microwave devices utilizing the surface wave suppression and the artificial magnetic conductor (AMC) properties of these special type metamaterial. Issues such as compactness, wide bandwidth with low attenuation level, tunability, and suitability with planar circuitry all play an important role in the design of EBG structures. Remarkable efforts have been undertaken for the development of EBG structures to be compatible with a wide range of wireless communication systems. This paper provides a comprehensive review on various EBG structures such as three-, two-, and onedimensional (3D, 2D, and 1D) EBG, mushroom and uniplanar EBG, and their successive advancement. Considering the related fabrication complexities, implementation of vialess EBG is an attractive topic for microwave engineers. For microstrip antennas, EBG structures are used in diversified ways, which of course found to be effective except in some cases. The EBG structures are also successfully utilized in antenna arrays for reducing the mutual coupling between elements of the array. Current challenges and limitations of the typical microstrip antennas and different EBG structures are discussed in details with some possible suggestions. Hopefully, this survey will guide to increasing efforts towards the development of more compact, wideband, and high-efficient uniplanar EBG structures for performance enhancement of antenna and other microwave devices.
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Neural networks are among the most accurate supervised learning methods in use today. However, their opacity makes them difficult to trust in critical applications, especially if conditions in training may differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions. These tools can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients. We apply these penalties both based on expert annotation and in an unsupervised fashion that produces multiple classifiers with qualitatively different decision boundaries. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test.
Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity

Transmembrane (TM) proteins represent about 20–30% of the protein sequences in higher eukaryotes, playing important roles across a range of cellular functions. Moreover, knowledge about topology of these proteins often provides crucial hints toward their function. Due to the difficulties in experimental structure determinations of TM protein, theoretical prediction methods are highly preferred in identifying the topology of newly found ones according to their primary sequences, useful in both basic research and drug discovery. In this paper, based on the concept of pseudo amino acid composition (PseAA) that can incorporate sequence-order information of a protein sequence so as to remarkably enhance the power of discrete models (Chou, K. C., Proteins: Structure, Function, and Genetics, 2001, 43: 246–255), cellular automata and Lempel-Ziv complexity are introduced to predict the TM regions of integral membrane proteins including both α-helical and β-barrel membrane proteins, validated by jackknife test. The result thus obtained is quite promising, which indicates that the current approach might be a quite potential high throughput tool in the post-genomic era. The source code and dataset are available for academic users at liml@scu.edu.cn.
Low-Rank Bilinear Pooling for Fine-Grained Classification

Pooling second-order local feature statistics to form a high-dimensional bilinear feature has been shown to achieve state-of-the-art performance on a variety of fine-grained classification tasks. To address the computational demands of high feature dimensionality, we propose to represent the covariance features as a matrix and apply a low-rank bilinear classifier. The resulting classifier can be evaluated without explicitly computing the bilinear feature map which allows for a large reduction in the compute time as well as decreasing the effective number of parameters to be learned. To further compress the model, we propose a classifier co-decomposition that factorizes the collection of bilinear classifiers into a common factor and compact per-class terms. The co-decomposition idea can be deployed through two convolutional layers and trained in an end-to-end architecture. We suggest a simple yet effective initialization that avoids explicitly first training and factorizing the larger bilinear classifiers. Through extensive experiments, we show that our model achieves state-of-the-art performance on several public datasets for fine-grained classification trained with only category labels. Importantly, our final model is an order of magnitude smaller than the recently proposed compact bilinear model [8], and three orders smaller than the standard bilinear CNN model [19].
A brief goal-setting and planning intervention to improve well-being for people with psychiatric disorders.

The aim of the current study was to develop the evidence for a brief intervention that targets goal-setting and planning skills (GAP) to improve well-being. Having goals for the future and being able to make progress towards them are key components of many theoretical approaches to well-being [9] and GAP has shown promising results with non-clinical populations [10] , as well as for people with depression [6] and forensic inpatients [7] . We investigated whether GAP could be effectively implemented for people with psychiatric disorders accessing specialist mental health services. As a secondary aim, the study design allowed for some exploration of the feasibility of mental health professionals incorporating a brief intervention focused on well-being into their routine practice. A total of 82 service users were recruited through a large mental health trust in the Greater London area. Participants were eligible for the study if they were: (1) aged 18 or over, (2) in contact (or eligible for contact) with specialist mental health services provided by the trust and (3) willing to take part in a group. Insufficient understanding of verbal and written English was used as an exclusion criterion due to the nature of the intervention. A cross-over design was used, with participants initially randomly allocated to either the intervention or a waiting-list control group. Participants allocated to the waiting-list condition were offered the intervention after they had completed their measures as controls and follow-up measures were taken from all participants 1 month after completing the intervention (see fig. 1 ). Recent policy developments in the UK [1, 2] and internationally [3] have highlighted the need for mental health professionals to adjust their working practices and place more emphasis on promoting well-being. However, it has also been recognised that workforce skills in this area may be lacking and that more emphasis should be placed on developing the evidence base to help mental health professionals promote well-being and support recovery [4] . The benefits of incorporating approaches from positive psychology into existing understandings of mental health problems have been discussed [4, 5] and there have been a few recent studies that have taken preliminary steps to establish the efficacy of interventions focused on well-being with particular clinical groups [6–8] . However, the research literature in this area is still very limited. Received: March 29, 2013 Accepted after revision: October 9, 2013 Published online: January 22, 2014
Lateral flow assay with near-infrared dye for multiplex detection.

BACKGROUND
Lateral flow assays (LFAs) are popular point-of-care diagnostic tools because they are rapid and easy to use. Nevertheless, they often lack analytical sensitivity and quantitative output and may be difficult to multiplex, limiting their usefulness in biomarker measurement. As a proof-of-concept study, we detail the design of a quantitative, multiplex LFA with readily available near-infrared (NIR) detection to improve analytical sensitivity.


METHODS
NIR dye was conjugated to selected antibodies and incorporated into LFAs. We used singleplex, optimized NIR-LFAs to measure interleukin (IL)-6 from 0 to 200 pg/mL and developed duplex assays to simultaneously measure IL-6 from 0 to 100 pg/mL (0 to 4.5 pmol/L) and C-reactive protein (CRP) from 50 to 2500 ng/mL (0.4 to 20 nmol/L) on a single test strip. Assays were tested on 60 different spiked samples and compared to ELISA results.


RESULTS
NIR-LFAs detected IL-6 in a 10% plasma matrix with a limit of detection of 4 pg/mL (182 fmol/L) and a CV <7%. Duplex NIR-LFAs quantitatively measured IL-6 and CRP concentrations simultaneously. Values strongly correlated to ELISA measurements, with R(2) values of 0.9825 and 0.9711 for IL-6 and CRP, respectively.


CONCLUSIONS
NIR-LFAs exhibit quantitative measurement at pg/mL concentrations owing to a high signal-to-background ratio and robust detection antibody clearance through the test strip. Moreover, NIR-LFAs are able to detect molecules present at vastly different concentrations in multiplex format and compare favorably to ELISAs. LFAs with direct NIR detection may be a valuable tool for biomarker evaluation in the point-of-care setting.
Rule-based machine learning classification and knowledge discovery for complex problems

Learning classifier systems (LCSs) are an advantageous, powerful, and flexible class of algorithms that have, to date, been underutilized largely due to the perception that they are difficult to apply, evaluate, and interpret. ExSTraCS is an Extended Supervised Tracking and Classifying System based on the Michigan-Style LCS architecture [4]. It offers an accessible, user friendly LCS platform for supervised rule-based machine learning, classification, data mining, prediction, and knowledge discovery. ExSTraCS seeks to make no assumptions about the data, and is therefore model free and particularly well suited to complex problems that are multi-factorial, interacting (non-linear), heterogeneous, noisy, class imbalanced, multi-class, or larger-scale. ExSTraCS is written in Python, open source, well documented, and freely available at sourceforge.net.
The interaction of representation and reasoning.

Automated reasoning is an enabling technology for many applications of informatics. These applications include verifying that a computer program meets its specification; enabling a robot to form a plan to achieve a task and answering questions by combining information from diverse sources, e.g. on the Internet, etc. How is automated reasoning possible? Firstly, knowledge of a domain must be stored in a computer, usually in the form of logical formulae. This knowledge might, for instance, have been entered manually, retrieved from the Internet or perceived in the environment via sensors, such as cameras. Secondly, rules of inference are applied to old knowledge to derive new knowledge. Automated reasoning techniques have been adapted from logic, a branch of mathematics that was originally designed to formalize the reasoning of humans, especially mathematicians. My special interest is in the way that representation and reasoning interact. Successful reasoning is dependent on appropriate representation of both knowledge and successful methods of reasoning. Failures of reasoning can suggest changes of representation. This process of representational change can also be automated. We will illustrate the automation of representational change by drawing on recent work in my research group.
Phase II trial of temozolomide plus concurrent whole-brain radiation followed by TNV regimen as adjuvant therapy for patients with newly diagnosed primary CNS lymphoma.

BACKGROUND
Primary central nervous system lymphoma (PCNSL) is an aggressive extranodal non-Hodgkin's lymphoma limited to the CNS. Treatment of PCNSL with high-dose methotrexate (HD-MTX)-based chemotherapy and whole-brain radiotherapy (WBRT) is associated with high rates of relapse and severe treatment-related neurotoxicity.


AIM
To report our experience of treating newly diagnosed PCNSL with temozolomide, nedaplatin, and vincristine (TNV), as the replacement of HD-MTX, in combination with concurrent chemoradiotherapy.


MATERIALS AND METHODS
Newly diagnosed PCNSL patients were given concurrent temozolomide (75 mg/m 2 , orally) daily during WBRT. Then, the TNV regimen was given after four weeks. The TNV regimen consisted of temozolomide (200 mg/m 2 orally: Days 1-5), nedaplatin (80 mg/m 2 intravenous: Day 1), and vincristine (1.4 mg/m 2 intravenous: Day 1). Each cycle was of a duration of four weeks and a maximum of six cycles were applied. The primary end point was response to treatment obtained by magnetic resonance imaging (MRI). Secondary end points were progression-free survival (PFS) and fewer toxic effects.


RESULTS
The study subjects included 14 patients (median age: 53.5, median Karnofsky Performance Scale (KPS): 75). The median number of TNV cycles given was five. RESPONSE TO TREATMENT: Complete response in 12 (85.7%) patients, partial response in 2 (14.3%) patients, and none with progressive disease. The objective response rate was 100%, and median PFS was 21.4 months. Toxicity was relatively mild, which mainly included nausea in six and fatigue in five, grade 3-4 hematotoxicity in one, and abnormal liver functions in five patients. No neurotoxicity has been observed till date.


CONCLUSION
The efficacy outcomes in this study are comparable to other reported HD-MTX-based regimens plus WBRT, with an added favorable toxicity profile. Prospective, randomized controlled trials are warranted to confirm such results.
Effects of once-weekly dosing of a long-acting release formulation of exenatide on glucose control and body weight in subjects with type 2 diabetes.

OBJECTIVE
In patients with type 2 diabetes, exenatide reduces A1C, postprandial and fasting glucose, and weight. In this study we investigated the effects of continuous exenatide administration from a long-acting release (LAR) formulation.


RESEARCH DESIGN AND METHODS
In this randomized, placebo-controlled phase 2 study, exenatide LAR (0.8 or 2.0 mg) was administered subcutaneously once weekly for 15 weeks to subjects with type 2 diabetes (n = 45) suboptimally controlled with metformin (60%) and/or diet and exercise (40%): 40% female, A1C (mean +/- SD) 8.5 +/- 1.2%, fasting plasma glucose 9.9 +/- 2.3 mmol/l, weight 106 +/- 20 kg, and diabetes duration 5 +/- 4 years.


RESULTS
From baseline to week 15, exenatide LAR reduced mean +/- SE A1C by -1.4 +/- 0.3% (0.8 mg) and -1.7 +/- 0.3% (2.0 mg), compared with +0.4 +/- 0.3% with placebo LAR (P < 0.0001 for both). A1C of < or =7% was achieved by 36 and 86% of subjects receiving 0.8 and 2.0 mg exenatide LAR, respectively, compared with 0% of subjects receiving placebo LAR. Fasting plasma glucose was reduced by -2.4 +/- 0.9 mmol/l (0.8 mg) and -2.2 +/- 0.5 mmol/l (2.0 mg) compared with +1.0 +/- 0.7 mmol/l with placebo LAR (P < 0.001 for both). Exenatide LAR reduced self-monitored postprandial hyperglycemia. Subjects receiving 2.0 mg exenatide LAR had body weight reductions (-3.8 +/- 1.4 kg) (P < 0.05), whereas body weight was unchanged with both placebo LAR and the 0.8-mg dose. Mild nausea was the most frequent adverse event. No subjects treated with exenatide LAR withdrew from the study.


CONCLUSIONS
Exenatide LAR offers the potential of 24-h glycemic control and weight reduction with a novel once-weekly treatment for type 2 diabetes.
Dermoscopy patterns of cicatricial alopecia resulting from discoid lupus erythematosus and lichen planopilaris.

BACKGROUND
Dermoscopy is an important tool for the diagnosis of benign and malignant melanocytic diseases. Recently, this method has also been found to be extremely useful in the diagnosis and follow-up of alopecias.


OBJECTIVE
The objective of this study was to describe dermoscopic findings in patients with clinical and histopathological characteristics of cicatricial alopecia.


METHODS
A descriptive cross-sectional study was conducted in which 14 patients with cicatricial alopecia were selected based on clinical and histopathological evaluation of the scalp. The underlying cause was classic lichen planopilaris in four cases, frontal fibrosing alopecia in five and discoid lupus erythematosus in the remaining five. The patients were evaluated using videodermoscopy and conventional dermoscopy (with a handheld dermoscope), performed independently by three different examiners. Magnification ranged from 10x to 70x.


RESULTS
Principal findings in cases of discoid lupus erythematosus were: white patches, branching capillaries, keratin plugs and areas of reduced follicular ostia; in classic lichen planopilaris: perifollicular scales, white dots and reduced follicular ostia; and in frontal fibrosing alopecia: reduced follicular ostia, perifollicular scales, perifollicular erythema and branching capillaries. The blue-grey dots described in this paper were a novel feature in scalp dermoscopy.


CONCLUSIONS
The use of dermoscopy for the clinical evaluation of the scalp in cases of cicatricial alopecia improves diagnostic capacity beyond simple clinical inspection and reveals novel features of the disease.
Digital breast tomosynthesis at screening assessment: are two views always necessary?

OBJECTIVE
The current recommendation from the UK National Health Service Breast Screening Programme is that digital breast tomosynthesis (DBT) can be used for further assessment of possible screen-detected soft-tissue abnormalities in place of spot compression views and when used should be performed in two projections. The aim of the study was to assess whether two-view DBT is necessary if the abnormality is seen only in one view on initial full-field digital mammography (FFDM).


METHODS
617 cases with possible masses, distortions and asymmetrical densities visualized only in one view on screening FFDM were included. All of these females underwent two-view DBT, clinical examination and ultrasound. The FFDM and DBT findings on each view were compared and correlated with the histological diagnosis.


RESULTS
586 of 617 cases had normal or benign findings on further assessment, and no additional information was obtained on the other DBT view. There were 31 confirmed cancers. In 26 cases (84%), the cancer was seen on the corresponding DBT view. No cancer was seen on the other DBT view alone. Five cancers (16%) were not seen on either view on DBT owing to technical reasons. No cancers would have been missed if only the corresponding DBT view was performed.


CONCLUSION
Two-view DBT may not be necessary when used for further assessment of possible screen-detected soft-tissue abnormalities. Larger studies should be undertaken to investigate this further.


ADVANCES IN KNOWLEDGE
One-view DBT may be adequate in assessing soft-tissue abnormalities seen only on one FFDM view.
Amino acids and derivatives, a new treatment of chronic heart failure?

Amino acids play a key role in multiple cellular processes. Amino acids availability is reduced in patients with heart failure (HF) with deleterious consequences on cardiac and whole-body metabolism. Several metabolic abnormalities have been identified in the failing heart, and many of them lead to an increased need of amino acids. Recently, several clinical trials have been conducted to demonstrate the benefits of amino acids supplementation in patients with HF. Although they have shown an improvement of exercise tolerance and, in some cases, of left ventricular function, they have many limitations, namely small sample size, differences in patients’ characteristics and nutritional supplementations, and lack of data regarding outcomes. Moreover recent data suggest that a multi-nutritional approach, including also antioxidants, vitamins, and metals, may be more effective. Larger trials are needed to ascertain safety, efficacy, and impact on prognosis of such an approach in HF.
The effect of liraglutide, a long-acting glucagon-like peptide 1 derivative, on glycemic control, body composition, and 24-h energy expenditure in patients with type 2 diabetes.

OBJECTIVE
Glucagon-like peptide (GLP)-1 is a gut hormone that exerts incretin effects and suppresses food intake in humans, but its therapeutic use is limited due to its short half-life. This was a randomized, double-blind, parallel-group, placebo-controlled trial investigating the effect of the long-acting GLP-1 derivative liraglutide (NN2211) on glycemic control, body weight, body composition, and 24-h energy expenditure in obese subjects with type 2 diabetes.


RESEARCH DESIGN AND METHODS
Thirty-three patients (mean +/- SD) aged 60.0 +/- 9.5 years, with HbA(1c) 7.5 +/- 1.2% and BMI 36.6 +/- 4.1 kg/m(2), were randomized to treatment with a single daily subcutaneous dose of 0.6 mg liraglutide (n = 21) or placebo (n = 12) for 8 weeks. In addition to weight and glycemic parameters, body composition was assessed by dual-energy X-ray absorptiometry (DEXA) scanning and 24-h energy expenditure in a respiratory chamber.


RESULTS
After 8 weeks, liraglutide reduced fasting serum glucose (liraglutide, -1.90 mmol/l, and placebo, 0.27 mmol/l; P = 0.002) and HbA(1c) (liraglutide, -0.33%, and placebo, 0.47%; P = 0.028) compared with placebo. No change in body weight was detected (liraglutide, -0.7 kg, and placebo, -0.9 kg; P = 0.756). There was a nonsignificant trend toward a decrease in total fat mass (liraglutide, -0.98%, and placebo, -0.12%; P = 0.088) and toward an increase in lean body mass (liraglutide, 1.02%, and placebo, 0.23%; P = 0.118) in the liraglutide group compared with the placebo group. Twenty-four-hour energy expenditure was unaffected by the treatment (liraglutide, -12.6 kJ/h, and placebo, -13.7 kJ/h; P = 0.799).


CONCLUSIONS
Eight weeks of 0.6-mg liraglutide treatment significantly improved glycemic control without increasing weight in subjects with type 2 diabetes compared with those on placebo. No influence on 24-h energy expenditure was detected.