Miracle-dz/UniFAR-FTUs huggingface dataset:
An Area-Efficient Column-Parallel Digital Decimation Filter With Pre-BWI Topology for CMOS Image Sensor

This paper presents an area-efficient design of a column-parallel second-order digital decimation filter for a <inline-formula> <tex-math notation="LaTeX">$\Sigma \Delta$ </tex-math></inline-formula> analog-to-digital converter-based CMOS image sensor. By using the proposed pre-bitwise-inversion topology having the same mathematical function, the chip area could be curtailed, where the bit-wise-inversion block, required by the correlated-double-sampling, is moved to the front of the adder. Following such a technique as this, more than six inverters and multiplexers can be reduced. This paper also proposes an adder scheme with only 118 transistors to implement the 13-bit dynamic range integrator. Moreover, the number of MSB cells inside the ripple counter is optimized to balance leakage current and active power consumption. Compared with the prior art, the proposed decimation filter core (without registers) can achieve a reduction of about 24% in transistor count. The proposed design is implemented with a 130-nm standard CMOS process. The total chip area of the digital decimation filter is only <inline-formula> <tex-math notation="LaTeX">$4.5\times 145~\mu \text{m}^{2}$ </tex-math></inline-formula> per column, which is less than half area compared against the prior art. According to the post-layout simulation, the maximum operating frequency is 124 MHz, and the total power consumption, including the filter core and 13-bit registers, is less than 6.35 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> under a typical 50-MHz clock frequency.
Cytotoxic and Antitumor Activity of the Extract of Clerodendron infortunatum: A Mechanistic Study

Objective: To investigate the molecular mechanism of anti tumor activity of Clerodendron infortunatum extract (CIE) under in vitro condition. Materials and Methods: The cytotoxic activity of CIE extract was tested on murine tumor cells (Dalton’s lymphoma ascites cells - DLA) and normal mouse cells (splenocytes) by trypan blue dye exclusion assay. The ability of the extract to induce apoptosis was measured via acridine orange and propidium iodide dual staining method. Apoptosis induction was confirmed by the analysis of level of expression of various apoptotic genes like bax , bcl-2 , caspase 8 and caspase 9 . Results: The extract showed a dose dependent anti-proliferative activity on DLA cells whereas mouse splenocytes were found to be not affected. Treatment of DLA cells with CIE increased the number of apoptotic events in DLA cells. Increase in the bax / bcl-2 ratio along with an escalation in the expression of caspase 9 by almost 60 fold confirmed the induction of intrinsic pathway of apoptosis. Conclusions: CIE showed effective anti-proliferative activity on DLA cells by the induction of intrinsic pathway of apoptosis.
Histological effect of monogenean parasites on gills of freshwater carps

Present investigation was carried out to study the histological effect of monogenetic parasite ( Paradactylogyrus sp. and Dactylogyrus sp.) on gills of freshwater cultivable carps . During present investigation Labeo rohita and Hypophthalmichthys moltrix were collected from eutrophic lakes (Lower Lake and Mansarovar Lake) of Bhopal region. Maximum prevalence was exhibited by Labeo rohita (20.0%) and least prevalence is recorded in H. molitrix (12.8%). Histological analysis revealed that the gills of H. molitrix was greatly affected which may be due to multiple infection by unidentified protozoan and Dactylogyrus sp. The pathological changes includes upliftment of respiratory epithelial layer, damaged cartilaginous core, deshaped secondary gill lamellae and damaged pillar cells.
FES-UPP: A Flexible Functional Electrical Stimulation System to Support Upper Limb Functional Activity Practice

There is good evidence supporting highly intensive, repetitive, activity-focused, voluntary-initiated practice as a key to driving recovery of upper limb function following stroke. Functional electrical stimulation (FES) offers a potential mechanism to efficiently deliver this type of therapy, but current commercial devices are too inflexible and/or insufficiently automated, in some cases requiring engineering support. In this paper, we report a new, flexible upper limb FES system, FES-UPP, which addresses the issues above. The FES-UPP system consists of a 5-channel stimulator running a flexible FES finite state machine (FSM) controller, the associated setup software that guides therapists through the setup of FSM controllers via five setup stages, and finally the Session Manager used to guide the patient in repeated attempts at the activities(s) and provide feedback on their performance. The FSM controller represents a functional activity as a sequence of movement phases. The output for each phase implements the stimulations to one or more muscles. Progression between movement phases is governed by user-defined rules. As part of a clinical investigation of the system, nine therapists used the FES-UPP system to set up FES-supported activities with twenty two patient participants with impaired upper-limbs. Therapists with little or no FES experience and without any programming skills could use the system in their usual clinical settings, without engineering support. Different functional activities, tailored to suit the upper limb impairment levels of each participant were used, in up to 8 sessions of FES-supported therapy per participant. The efficiency of delivery of the therapy using FES-UPP was promising when compared with published data on traditional face-face therapy. The FES-UPP system described in this paper has been shown to allow therapists with little or no FES experience and without any programming skills to set up state-machine FES controllers bespoke to the patient’s impairment patterns and activity requirements, without engineering support. The clinical results demonstrated that the system can be used to efficiently deliver high intensity, activity-focused therapy. Nevertheless, further work to reduce setup time is still required.
Episodic Arctic CO2 Limitation in the West Svalbard Shelf

The European Sector of the Arctic Ocean is characterized by low CO2 concentrations in seawater during spring and summer, largely due to strong biological uptake driven by extensive plankton blooms in spring. The spring plankton bloom is eventually terminated by nutrient depletion and grazing. However, low CO2 concentrations in seawater and low atmospheric resupply of CO2 can cause episodes during which the phytoplankton growth is limited by CO2. Here, we show that gross primary production (GPP) of Arctic plankton communities increases from 32 to 72 % on average with CO2 additions in spring. Enhanced GPP with CO2 additions occur during episodes of high productivity, low CO2 concentration and in the presence of dissolved inorganic nutrients. However, during summer the addition of CO2 supresses planktonic Arctic GPP. Events of CO2 limitation in spring may contribute to the termination of the Arctic spring plankton blooms. The stimulation of GPP by CO2 during the spring bloom provides a biotic feedback loop that might influence the global role played by the Arctic Ocean as a CO2 sink in the future.
Control of Cryptococcus Gattii Biofilms by an Ethanolic Extract of Cochlospermum Regium (Schrank) Pilger Leaves

Cryptococcus gattii is an etiologic agent of cryptococcosis and a serious disease that affects immunocompromised and immunocompetent patients worldwide. The therapeutic arsenal used to treat cryptococcosis is limited to a few antifungal agents, and the ability of C. gattii to form biofilms may hinder treatment and decrease its susceptibility to antifungal agents. The objective of this study was to evaluate the antifungal and antibiofilm activities of an ethanolic extract of Cochlospermum regium (Schrank) Pilger leaves against C. gattii. The antifungal activity was assessed by measuring the minimum inhibitory concentration (MIC) using the broth microdilution technique and interaction of the extract with fluconazole was performed of checkerboard assay. The antibiofilm activity of the extract was evaluated in 96-well polystyrene microplates, and the biofilms were quantified by counting colony forming units. The extract showed antifungal activity at concentrations of 62.5 to 250 μg/mL and when the extract was evaluated in combination with fluconazole, C. gattii was inhibited at sub-MIC levels. The antibiofilm activity of the extract against C. gattii was observed both during biofilm formation and on an already established biofilm. The results showed that the ethanolic extract of the leaves of C. regium shows promise for the development of antifungal drugs to treat cryptococcosis and to combat C. gattii biofilms.
Minimizing Energy Consumptions in User-Centric Ultra-Dense Networks

In this paper, we study the optimum designs of the downlink of energy efficient user-centric ultra- dense networks (UUDNs), where a large number of low power access points are densely deployed in the network to provide service to a large number of user equipment (UEs). In UUDN, the network architecture is shifted from traditional cell-centric to user- centric, where each UE can be served with many low power APs, with the density of APs higher than that of UEs. One of the main challenges faced by UUDN design is the high energy consumption due to the dense AP deployments. The objective of this paper is to identify the optimum AP-UE association that can minimize the total energy consumption of all APs, subject to the quality of service (QoS) constraints for each UE in the network. The design is formulated as an integer programming problem by employing maximal ratio transmission (MRT) among APs serving the same UE, and it is known to be NP-hard. To tackle this problem, we propose a low complexity relaxed gradient search (RGS) algorithm. In RGS, we first obtain a superset of the problem space by relaxing some of the non-convex constraints, then perform gradient-based search in the relaxed superset by exploiting its unique structure while ensuring all constraints in the original problem are satisfied. Simulation results show that the proposed algorithm outperforms other low complexity algorithms such as relaxed integer programming (RIP) and genetic algorithm (GA).
Determinants of Contraceptive Usage among Married Women in Shiraz, Iran

Background & aim: Contraceptive usage is a central part of the quality of care in the provision of family services. Currently, this issue has gained much importance since the Iranian policy makers are changing their policies about family planning and contraceptives accessibility. Regarding this, the aim of this study was to determine the rate of contraceptive usage and the factors affecting contraceptive use among the married women of reproductive age in Shiraz County, Iran. Methods: This cross-sectional study was conducted on 626 married women of reproductive age (i.e., 15-49 years) living in Shiraz County (including Shiraz city and the rural areas) using the quantitative survey method. Sampling was performed using multi-stage cluster and purposive sampling techniques. The sample size was determined based on the Krejcie and Morgan’s formula. The data were collected through a questionnaire filled out by some interviewers. Data analysis was performed both descriptively (i.e., frequency and percentage) and analytically (i.e., Chi-square and logistic regression tests) using the SPSS version 20. Results: According to the results of this study, the main predictors of contraceptive usage were couple agreement on contraception method, the number of actual births, women’s authority, knowledge and positive attitude about contraceptives, and the number of desired children. Conclusion: Regarding the new population policies, the Iranian policy makers should be aware of the different aspects of family planning programs, particularly those targeting the contraceptive usage.
Review on Some Malaysian Traditional Medicinal Plants with Therapeutic Properties

There are many medicinal plants that have been used for thousands of years. These plants can now be found in herbal products and as part of the traditional Malaysian health care system because of their therapeutic efficacy. This paper discusses the use of ten medicinal plants in the traditional medicinal system of Malaysia and related scientific studies on their Pharmaceutical properties, which demonstrate their traditional uses. The plants viewed are Amaranthus spinosus L. , Arundina graminifolia (D.Don) Hochr. , Callicarpa arborea Roxb. , Carica papaya L. , Citrus grandis L. , Coleus amboinicus Lour. , Curcuma domestica Valeton. , Psidium guajava L. , Garcinia atroviridis Griff. ex T. Anders. , and Zingiber officinale Rosc. All the plants mentioned in this paper have therapeutic properties, which explains and validates their uses in traditional medicine.
Cerebral small vessel disease in Indonesia: Lacunar infarction study from Indonesian Stroke Registry 2012–2014

Background and Purpose: Stroke is the number one cause of morbidity and mortality in Indonesia. Lacunar infarction is one of cerebral small vessel disease spectrum. This study aimed to present stroke epidemiology in Indonesia and risk factors associated with cerebral small vessel disease. Methods: A multicenter prospective cross-sectional study of 18 hospitals in Indonesia was conducted using Stroke Case Report Form from 2012 to 2014. Stroke was diagnosed based on clinical findings confirmed with non-contrast computed tomography of the brain. Subjects were classified into two large groups: ischemic (lacunar and non-lacunar) and hemorrhagic (intracranial and subarachnoid hemorrhage). Other risk factors were assessed on admission. Results: We enrolled 5411 patients, of whom 3627 (67.03%) had ischemic stroke and 1784 (32.97%) had hemorrhagic stroke. Male patients were prevalent in both large groups, although found less in subarachnoid hemorrhage group. Among patients with hemorrhagic stroke, 1603 (89.54%) of them had intracerebral hemorrhage and 181 (10.46%) had subarachnoid hemorrhage. From 3627 ischemic stroke patients, 1635 (45.07%) of them had lacunar infarction. We found that age above 55 years old, male gender, hypertension, dyslipidemia, and diabetes were important risk factors associated with lacunar stroke (p < 0.05). Conclusion: Ischemic stroke was the leading cause of stroke in Indonesia. In total, 45% of the total ischemic stroke patients had lacunar infarction. Important risk factors associated with lacunar infarction were hypertension, dyslipidemia, diabetes, age over 55, and male population.
1 Management practices : Are not for profits different ?

Recent studies have demonstrated the importance of good management for firm performance. Here, we focus on management in not-for-profits (NFPs). We present a model predicting that management quality will be lower in NFPs compared to for-profits (FPs), but that outputs may not be worse if managers are altruistic. Using a tried and tested survey of management practices, we find that NFPs score lower than FPs but also that, while the relationship between management scores and outputs holds for FPs, the same is not true for NFPs. One implication is that management practices that work for FPs may be less effective in driving performance in NFPs.
The role of robot design in decoding error-related information from EEG signals of a human observer

For utilization of robotic assistive devices in everyday life, means for detection and processing of erroneous robot actions are a focal aspect in the development of collaborative systems, especially when controlled via brain signals. Though, the variety of possible scenarios and the diversity of used robotic systems pose a challenge for error decoding from recordings of brain signals such as via EEG. For example, it is unclear whether humanoid appearances of robotic assistants have an influence on the performance. In this paper, we designed a study in which two different robots executed the same task both in an erroneous and a correct manner. We find error-related EEG signals of human observers indicating that the performance of the error decoding was independent of robot design. However, we can show that it was possible to identify which robot performed the instructed task by means of the EEG signals. In this case, deep convolutional neural networks (deep ConvNets) could reach significantly higher accuracies than both regularized Linear Discriminanat Analysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined with rLDA. Our findings indicate that decoding information about robot action success from the EEG, particularly when using deep neural networks, may be an applicable approach for a broad range of robot designs.
Reputation Value Ranking Based Sequential Cooperative Spectrum Sensing against Byzantine Attack

Cooperative spectrum sensing (CSS) has emerged as a key solution to effectively improve the sensing accuracy in cognitive radio networks (CRNs). But it can be threatened by malicious users (MUs) launching Byzantine attack. Motivated by the fact that the existing Byzantine mitigation schemes have strong assumptions such as the MUs are in minority or act in a simplified attack strategy, this paper firstly focuses on the issue of designing a general and reliable reference for CSS. By analyzing sophisticated malicious behaviors, we formulate a random Byzantine attack model, which goes beyond the existing models for its generalization. Under this generalized attack model, we make use of delivery-based assessment (DBA) to check consistency of the local reports from secondary users (SUs) regarding the presence of the phenomenon and innovatively exploit the reports falsified by MUs. Then, all reports in a reputation value (RV) order are sequentially processed until the sequential test terminates. At last, numerical results are presented that show our proposed scheme significantly outperforms weighted sequential probability ratio test (WSPRT) in cooperative sensing performance regardless of what type of attack strategy, while hugely reduces the number of samples required at the fusion center (FC).
Risk Factors for Deep Vein Thrombosis or Pulmonary Embolus Following Anterior Cruciate Ligament Reconstruction

Background: Nearly 350,000 Americans develop a deep venous thromboembolism (DVT) or pulmonary embolism (PE) annually, and nearly 100,000 Americans die from these events. To date, little research has investigated patient-specific risk factors that increase the rate of DVT/PE following anterior cruciate ligament reconstruction (ACLR). Purpose: To determine relevant patient risk factors for the development of DVT/PE following ACLR. Study Design: Case-control study; Level of evidence, 3. Methods: All instances of ACLR from 2005 to 2014 within the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) prospective database were analyzed. Both univariate analysis and binary logistic regression were performed to determine which patient demographics and surgical factors were associated with DVT or PE following surgery. Results: Of the 9146 patients who underwent ACLR, 46 (0.5%) developed postoperative DVT, 8 (0.1%) developed PE, and 5 (0.05%) developed both. The following variables were associated with the development of DVT or PE on univariate analysis: increased age, a high tibial osteotomy (HTO) performed at the time of ACLR, microfracture performed, the presence of hypertension requiring medical therapy, and the presence of an active wound infection. Independent predictors of DVT or PE on multivariate analysis included HTO (odds ratio [OR], 22.7), the presence of an active wound infection (OR, 11.0), or hypertension requiring medication (OR, 2.2). Meniscal repair was not a risk factor for DVT or PE on univariate or multivariate analysis. Conclusion: In a review of 9146 patients undergoing ACLR, 46 (0.5%) developed DVT in the 30-day postoperative period. Increasing age over 30 years, concomitant HTO or microfracture, hypertension requiring medication, and presence of wound infection were all associated with an increased risk of DVT. The annual incidence of DVT/PE following ACLR reconstruction is low (<1%) and has not changed over time.
The relationship between the expression of thymidylate synthase, dihydropyrimidine dehydrogenase, orotate phosphoribosyltransferase, excision repair cross-complementation group 1 and class III β-tubulin, and the therapeutic effect of S-1 or carboplatin plus paclitaxel in non-small-cell lung cancer

Previous studies have reported that the expressions of specific proteins may predict the efficacy of chemotherapy agents for non-small cell lung cancer (NSCLC) patients. The present study evaluated the expression of proteins hypothesized to be associated with the effect of chemotherapeutic agents in 38 NSCLC patients with pathological stage II and IIIA. The subjects received carboplatin plus paclitaxel (CP) or S-1 as adjuvant chemotherapy following complete resection. The protein expressions evaluated were those of thymidylate synthase (TS), dihydropyrimidine dehydrogenase (DPD) and orotate phsphoribosyltransferase (OPRT), which were suspected to be associated with the effect of S-1 agents, excision repair cross-complementation group 1 (ERCC1), which was suspected to be associated with the effect of platinum-based agents, and class III β-tubulin (TUBB3), which was suspected to be associated with the effect of taxane-based agents. The positive rate of TS was 55.3% (n=21/38), DPD was 57.9% (n=22/38), OPRT was 42.1% (n=16/38), ERCC1 was 47.4% (n=18/38) and TUBB3 was 44.7% (n=17/38). Among the patients who received S-1 adjuvant chemotherapy, TS-negative cases demonstrated a significantly better disease-free survival than positive cases. Thus, TS protein expression may have been a factor that predicted the effect of S-1 agent as adjuvant chemotherapy.
Sparse Deep Stacking Network for Fault Diagnosis of Motor

A sparse deep learning method is proposed to overcome overfitting risk of deep networks with a large number of nodes and layers. Deep stacking network (DSN) is a classic and effective deep learning method, and its sparse form is presented to generate the sparse deep learning method. In DSN, output labels are encoded as a series consisted of 1 and 0. This coding strategy makes output labels to be sparse. However, sparsity of output labels is not considered in DSN model. Considering this limitation, sparse DSN (SDSN) is developed in this paper. The SDSN extends tradition DSN in sparsity characterization using a sparse regularization term. By this term, predicted output label is constrained to be similar with ideal output label that is binary and consisted of continuous 1 and 0 with a sidestep shape. The sparse regularization term is used as a soft threshold strategy to set irrelevant element to be zero, by which effectiveness of SDSN is enhanced. Case studies about fault diagnosis of motor are used to validate performance of SDSN. Comparison between SDSN and commonly used deep networks is further conducted. The results show advance of SDSN for fault classification.