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Type-2 diabetes mellitus (T2DM) is a medical condition in which oral medications avail to patients to curb their hyperglycaemia after failed dietary therapy. However, individual responses to the prescribed pharmacotherapy may differ due to their clinical profiles, comorbidities, lifestyles and medical adherence. One approach is to identify similar patients within the same community to predict their likely response to the prescribed diabetes medications. This study aims to present an evidence-based diabetes medication recommendation system (DMRS) underpinned by patient similarity analytics. The DMRS was developed using 10-year electronic health records of 54,933 adult patients with T2DM from six primary care clinics in Singapore. Multiple clinical variables including patient demographics, comorbidities, laboratory test results, existing medications, and trajectory patterns of haemoglobin A1c (HbA1c) were used to identify similar patients. The DMRS was evaluated on four groups of patients with comorbidities such as hyperlipidaemia (HLD) and hypertension (HTN). Recommendations were assessed using hit ratio which represents the percentage of patients with at least one recommended sets of medication matches exactly the diabetes prescriptions in both the type and dosage. Recall, precision, and mean reciprocal ranking of the recommendation against the diabetes prescriptions in the EHR records were also computed. Evaluation against the EHR prescriptions revealed that the DMRS recommendations can achieve hit ratio of 81% for diabetes patients with no comorbidity, 84% for those with HLD, 78% for those with HTN, and 75% for those with both HLD and HTN. By considering patients’ clinical profiles and their trajectory patterns of HbA1c, the DMRS can provide an individualized recommendation that resembles the actual prescribed medication and dosage. Such a system is useful as a shared decision-making tool to assist clinicians in selecting the appropriate medications for patients with T2DM.

The histone variant H2AZ is overexpressed in diverse cancer types where it facilitates the accessibility of transcriptional regulators to the promoters of cell cycle genes. However, the molecular basis for its dysregulation in cancer remains unknown. Here, we report that glioblastomas (GBM) and glioma stem cells (GSCs) preferentially overexpress H2AZ for their proliferation, stemness and tumorigenicity. Chromatin accessibility analysis of H2AZ2 depleted GSC revealed that E2F1 occupies the enhancer region within H2AZ2 gene promoter, thereby activating H2AZ2 transcription. Exploration of other H2AZ2 transcriptional activators using a customized “anti-H2AZ2” query signature for connectivity map analysis identified STAT3. Co-targeting E2F and STAT3 synergistically reduced the levels of H2AZ, histone 3 lysine 27 acetylation (H3K27ac) and cell cycle gene transcription, indicating that E2F1 and STAT3 synergize to activate H2AZ gene transcription in GSCs. Remarkably, an E2F/STAT3 inhibitor combination durably suppresses GSC tumorigenicity in an orthotopic GBM xenograft model. In glioma patients, high STAT3 signaling is associated with high E2F1 and H2AZ2 expression. Thus, GBM has uniquely opted the use of E2F1- and STAT3-containing “enhanceosomes” that integrate multiple signaling pathways to achieve H2AZ gene activation, supporting a translational path for the E2F/STAT3 inhibitor combination to be applied in GBM treatment.

In this study, the degradation efficiency and electricity generation of the azo dyes affected by the functional groups and molecular structure in a solar photocatalytic fuel cell (PFC) system were investigated and discussed in detail. Four different azo dyes such as, Acid Orange 7 (AO7), Acid Red 18 (AR18), Reactive Black 5 (RB5), Reactive Red 120 (RR120) with different molecular structure were evaluated. The degradation efficiency of AO7, AR18, RB5 and RR120 achieved 5.6 ± 0.3%, 11.1 ± 0.6%, 41.9 ± 0.9% and 52.1 ± 1.3%, respectively, after 6 h irradiated under solar light. In addition, the maximum power density, P(max) for AO7, AR18, RB5 and RR120 was 0.0269 ± 0.01, 0.111 ± 0.03, 1.665 ± 0.67 and 4.806 ± 1.79 mW cm(−2), respectively. Meanwhile, the concentration of COD for AO7, AR18, RB5 and RR120 reduced to 16 ± 0.1, 10 ± 0.3, 7 ± 0.6 and 3 ± 0.9 mg L(−1), respectively. The concentration ratio of benzene / naphthalene, benzene / azo bond and naphthalene / azo bond, respectively, was analyzed to investigate the impact of the functional groups over photodegradation of the azo dyes in PFC. Electron releasing groups (-OH and –NH(2)) and electron withdrawing groups (-SO(3)Na) which attached to the naphthalene or benzene ring also played a pivotal role in the degradation mechanism.

Background Clinical trials have demonstrated that either initiating or up-titrating a statin dose substantially reduce Low-Density Lipoprotein-Cholesterol (LDL-C) levels. However, statin adherence in actual practice tends to be suboptimal, leading to diminished effectiveness. This study aims to use real-world data to determine the effect on LDL-C levels and LDL-C goal attainment rates, when selected statins are titrated in Asian patients. Methods A retrospective cohort study over a 5-year period, from April 2014 to March 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of Dyslipidaemia in a primary care clinic in Singapore. The statins were classified into low-intensity (LI), moderate-intensity (MI) and high-intensity (HI) groups according to the 2018 American College of Cardiology and American Heart Association (ACC/AHA) Blood Cholesterol Guidelines. Patients were grouped into “No statin”, “Non-titrators” and “Titrators” cohorts based on prescribing patterns. For the “Titrators” cohort, the mean percentage change in LDL-C and absolute change in LDL-C goal attainment rates were computed for each permutation of statin intensity titration. Results Among the cohort of 11,499 patients, with a total of 266,762 visits, there were 1962 pairs of LDL-C values associated with a statin titration. Initiation of LI, MI and HI statin resulted in a lowering of LDL-C by 21.6% (95%CI = 18.9–24.3%), 28.9% (95%CI = 25.0–32.7%) and 25.2% (95%CI = 12.8–37.7%) respectively. These were comparatively lower than results from clinical trials (30 to 63%). The change of LDL-C levels due to up-titration, down-titration, and discontinuation were − 12.4% to − 28.9%, + 13.2% to + 24.6%, and + 18.1% to + 32.1% respectively. The improvement in LDL-C goal attainment ranged from 26.5% to 47.1% when statin intensity was up-titrated. Conclusion In this study based on real-world data of Asian patients in primary care, it was shown that although statin titration substantially affected LDL-C levels and LDL-C goal attainment rates, the magnitude was lower than results reported from clinical trials. These results should be taken into consideration and provide further insight to clinicians when making statin adjustment recommendations in order to achieve LDL-C targets in clinical practice, particularly for Asian populations.

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p p p

We report the extremely large magnetoresistance and anisotropic magnetoresistance in a non-magnetic semimetallic NbAs2 single crystal. Unsaturated transverse XMR with quadratic field dependence has been observed to be ~3 × 105 % at 2 K and 15 T. Up to 12.5 K, clear Shubnikov de Haas (SdH) quantum oscillations were observed from which two distinct Fermi pockets were identified. The corresponding quantum electronic parameters such as effective cyclotron mass and Dingle temperature were obtained using Lifshitz-Kosevich formula. From the field dependent Hall resistivity at 2 K, carrier concentrations n e (n h ) = 6.7691 (6.4352) × 1025 m−3 and mobilities μ e (μ h ) = 5.6676 (7.6947) m2 V−1 s−1 for electrons (e) and holes (h) were extracted using semiclassical two-band model fitting. We observed large anisotropic magnetoresistance about 84%, 75%, and 12% at 0.75 T and 6 K for three different orientations γ, θ and ϕ, respectively, similar to that in several topological semimetallic systems. Magnetic properties of NbAs2 are similar to the case of graphite, without any phase transition in the temperature range from 5 K to 300 K.

Background Alternating wet and dry seasons may play an important role in the acquisition and distribution of Blastocystis subtype infection in the tropics. This cross-sectional study was therefore conducted to provide the prevalence of Blastocystis and to determine the potential risk factors associated with each subtype during the wet and dry seasons in the Aboriginal community, Pahang, Malaysia. Methods A total of 473 faecal samples were collected: 256 (54.1%) and 217 (45.9%) samples were obtained during the wet (October-November 2014) and the dry season (June 2015), respectively. All fresh faecal samples were subjected to molecular analysis for subtype and allele identification. Results Of the 473 samples, 42.6% and 37.8% were positive for Blastocystis ST1, ST2, ST3 and ST4 during wet and dry seasons, respectively. Prevalence of Blastocystis ST1 was significantly higher during the wet season compared to the dry season (Z = 2.146, P

Many applications require understanding how event occurrences at one geographical region affect or influence event occurrences at another region, e.g. spread of disease and forest fires. Existing works typically impose a grid to partition the spatial space and utilize spatial autocorrelation property to model the spatial dependency among the grid cells. However, they are often highly sensitive to the granularity of the grid size and they do not incorporate the temporal dynamics of the event occurrences among regions. This paper utilizes the notion of a spatial network with temporal dependency to capture the dynamics of event occurrences among regions. This network is modeled as a directed graph where each node is a group of spatially nearby events and each directed edge represents the influence of events from a source node to a destination node. We design an algorithm called LinkNet to generate this network from spatio-temporal event databases. LinkNet utilizes minimum description length based information---theoretic approach to automatically adjust the number of regions and the temporal relationships among regions. Two optimizations are devised to reduce the computational complexity of LinkNet. We also demonstrate how the proposed network can be used for hotspot prediction. Experiment results on both synthetic and real world datasets demonstrate the efficiency of LinkNet and the effectiveness of the network in predicting the next hotspots.

Cadmium arsenide ($\rm Cd_3As_2$) is known for its inverted band structure and ultra-high electron mobility. It has been theoretically predicted and also confirmed by ARPES experiments to exhibit a 3D Dirac semimetal phase containing degenerate Weyl nodes. From magneto-transport measurements in high quality single crystals of $\rm Cd_3As_2$, a small effective mass $m^* \approx$ 0.05 $m_e$ is determined from the Shubnikov-de Haas (SdH) oscillations. In certain field orientations, we find a splitting of the SdH oscillation frequency in the FFT spectrum suggesting a possible lifting of the double degeneracy in accord with the helical spin texture at outer and inner Fermi surfaces with opposite chirality predicted by our \textit{ab initio} calculations. Strikingly, a large antisymmetric magnetoresistance with respect to the applied magnetic fields is uncovered over a wide temperature range in needle crystal of $\rm Cd_3As_2$ with its long axis along [112] crystal direction. It reveals a possible contribution of intrinsic anomalous velocity term in the transport equation resulting from a unique 3D Rashba-like spin splitted bands that can be obtained from band calculations with the inclusion of Cd antisite defects., Comment: 17 pages, 5 figures

This work reports how novel multi-layered (from double-layered to quadruple-layered) microparticles comprising immiscible polymers can be fabricated through a simple, economical, reliable and versatile one-step solvent evaporation method. These multi-layered microparticles would be excellent candidates to overcome problems inherent in single-layered microparticles for drug delivery. Particle morphologies, layer configurations, and drug distribution were determined by scanning electron microscopy and Raman mapping. Key process parameters achieving the formation of the multi-layered structure were identified. Encapsulation of multiple drugs and layer localization of these drugs within these multi-layered microparticles have also shown to be possible, which were driven by drug-polymer affinity. This one-step fabrication technique can therefore be used for tailoring particle designs, thus facilitating the development of multiparticulate drug delivery devices. Accepted version

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Zooplankton, sampled at five stations from the upper Sangga estuary (7 km upstream) in Matang Mangrove Forest Reserve (MMFR), Malaysia, to 16 km offshore, comprised more than 47% copepod. Copepod abundance was highest at nearshore waters (20,311 ind m−3), but decreased toward both upstream (15,572 ind m−3) and offshore waters (12,330 ind m−3). Copepod abundance was also higher during the wetter NE monsoon period as compared to the drier SW monsoon period, but vice versa for copepod species diversity. Redundancy analysis (RDA) shows that copepod community structure in the upper estuary, nearshore and offshore waters differed, being influenced by spatial and seasonal variations in environmental conditions. The copepods could generally be grouped into estuarine species (dominantly Acartia spinicauda Mori, Acartia sp1, Oithona aruensis Fruchtl, and Oithona dissimilis Lindberg), stenohaline species (Acartia erythraea Giesbrecht, Acrocalanus gibber Giesbrecht, Paracalanus aculateus Giesbrecht, and Corycaeus andrewsi Farran) and euryhaline species (Parvocalanus crassirostris Dahl, Oithona simplex Farran, and Bestiolina similis (Sewell)). Shifts in copepod community structure due to monsoonal effects on water parameters occurred at the lower estuary. Copepod peak abundance in mangrove waters could be associated with the peak chlorophyll a concentration prior to it. Evidence of copepod consumption by many species of young fish and shrimp larvae in the MMFR estuary implies the considerable impact of phytoplankton and microphytobenthos on mangrove trophodynamics.

This paper presents a novel variable gain amplifier (VGA) design which is applied in the automatic gain control loop of digital video broadcasting (DVB) receivers designed for standards DVB-T/H. The VGA is composed of three cascaded tunable gain stages to provide a 70 dB dynamic range with 2 dB gain steps. Each gain stage is based on a digital variable gain amplifier (DVGA) which is composed of a plurality of gain blocks (GBs) and a fully differential degeneration amplifier (FDDA). The GBs of the DVGA are digitally controlled current mirrors. A common-mode feedback circuitry is used to stabilize the FDDA by providing a stable common-mode voltage. Physical measurements on silicon show that the gain error is merely 0.55 dB given a 70 dB tuning range.

Genome editing driven by zinc-finger nucleases (ZFNs) yields high gene-modification efficiencies (>10%) by introducing a recombinogenic double-strand break into the targeted gene. The cleavage event is induced using two custom-designed ZFNs that heterodimerize upon binding DNA to form a catalytically active nuclease complex. Using the current ZFN architecture, however, cleavage-competent homodimers may also form that can limit safety or efficacy via off-target cleavage. Here we develop an improved ZFN architecture that eliminates this problem. Using structure-based design, we engineer two variant ZFNs that efficiently cleave DNA only when paired as a heterodimer. These ZFNs modify a native endogenous locus as efficiently as the parental architecture, but with a >40-fold reduction in homodimer function and much lower levels of genome-wide cleavage. This architecture provides a general means for improving the specificity of ZFNs as gene modification reagents.

The three dimensional (3D) Dirac semimetal is a new quantum state of matter that has attracted much attention recently in physics and material science. Here, we report on the growth of large plate-like single crystals of Cd3As2 in two major orientations by a self-selecting vapor growth (SSVG) method and the optimum growth conditions have been experimentally determined. The crystalline imperfections and electrical properties of the crystals were examined with transmission electron microscopy (TEM), scanning tunneling microscopy (STM) and transport property measurements. This SSVG method makes it possible to control the as-grown crystal compositions with excess Cd or As leading to mobilities near 5–105 cm2V−1s−1. Zn-doping can effectively reduce the carrier density to reach the maximum residual resistivity ratio (RRR"Equation missing"ρ300K/ρ5K) of 7.6. A vacuum-cleaved single crystal has been investigated using angle-resolved photoemission spectroscopy (ARPES) to reveal a single Dirac cone near the center of the surface Brillouin zone with a binding energy of approximately 200 meV.

Permanent modification of the human genome in vivo is impractical owing to the low frequency of homologous recombination in human cells, a fact that hampers biomedical research and progress towards safe and effective gene therapy. Here we report a general solution using two fundamental biological processes: DNA recognition by C2H2 zinc-finger proteins and homology-directed repair of DNA double-strand breaks. Zinc-finger proteins engineered to recognize a unique chromosomal site can be fused to a nuclease domain, and a double-strand break induced by the resulting zinc-finger nuclease can create specific sequence alterations by stimulating homologous recombination between the chromosome and an extrachromosomal DNA donor. We show that zinc-finger nucleases designed against an X-linked severe combined immune deficiency (SCID) mutation in the IL2Rgamma gene yielded more than 18% gene-modified human cells without selection. Remarkably, about 7% of the cells acquired the desired genetic modification on both X chromosomes, with cell genotype accurately reflected at the messenger RNA and protein levels. We observe comparably high frequencies in human T cells, raising the possibility of strategies based on zinc-finger nucleases for the treatment of disease.

Efficient clustering in dynamic spatial databases is currently an open problem with many potential applications. Most traditional spatial clustering algorithms are inadequate because they do not have an efficient support for incremental clustering.In this paper, we propose DClust, a novel clustering technique for dynamic spatial databases. DClust is able to provide multi-resolution view of the clusters, generate arbitrary shapes clusters in the presence of noise, generate clusters that are insensitive to ordering of input data and support incremental clustering efficiently. DClust utilizes the density criterion that captures arbitrary cluster shapes and sizes to select a number of representative points, and builds the Minimum Spanning Tree (MST) of these representative points, called R-MST. After the initial clustering, a summary of the cluster structure is built. This summary enables quick localization of the effect of data updates on the current set of clusters. Our experimental results show that DClust outperforms existing spatial clustering methods such as DBSCAN, C2P, DENCLUE, Incremental DBSCAN and BIRCH in terms of clustering time and accuracy of clusters found.

A vision-based soccer robot system is proposed, in which vision will identify the position and heading angle of each robot, and the position of the ball. With these imaging data, values for the defense factor, the competition factor, and the angle factor, are obtained. Using the three factors as the input variables of the proposed action selection mechanism, which is expressed in terms of fuzzy rules, the action that each robot should take is then chosen from the five basic actions available for the robots. In this manner, each robot may intercept, shoot, block, sweep the ball, or just stand by. After determining the action of each robot, control commands generated by the host computer are sent to the robot directly through a wireless RS-232. To show the feasibility of the proposed method, experimental results of a robot soccer game will be used for illustration.

We study the one-dimensional deformed $t-t^{\prime}-J$ model in terms of the continuum field theories. We found that at low doping concentration and far away from the phase separation regime, there are two phases: the Luttinger liquid and the Luther-Emery liquid, depending on t'/t (t'/t)_c, where (t'/t)_c>0. Moreover, the singlet superconducting correlations are dominant in the Luther-Emery liquid.

A fuzzy algorithm is proposed to navigate a mobile robot from a given initial configuration to a desired final configuration in an unknown environment filled with obstacles. The mobile robot is equipped with an electronic compass and two optical encoders for dead-reckoning, and two ultrasonic modules for self-localization and environment recognition. From the readings of sensors at every sampling instant, the proposed fuzzy algorithm will determine the priorities of thirteen possible heading directions. Then the robot is driven to an intermediate configuration along the heading direction that has the highest priority. The navigation procedure will be iterated until a collision-free path between the initial and the final configurations is found. To show the feasibility of the proposed method, in addition to computer simulation, experimental results will be also given.

Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining.

We develop a logic for entity-relationship databases, ERL, that is a generalization of database logic. ERL provides advantages to the ER model much as FOL (first-order logic) does to the relational model: a uniform language for expressing database schema, integrity constraints, and database manipulation; clearly defined semantics; the capability to express database transformations; and deductive capabilities. We propose three query languages for ER databases called ERRC, ERSQL, and ERQBE, which are generalizations of the relational calculus, SQL, and QBE, respectively. We use example queries and updates to demonstrate the capabilities of these languages. We apply database transformations to introduce the notion of views and to show that both ERRC and ERSQL are relationally complete.

The immunological adjuvant, Propionibacterium acnes (formerly classified as Corynebacterium parvum) is believed to be the major factor in the etiology of inflammatory acne (IA). We have previously reported defective phagocytosis of P. acnes by neutrophils in some patients with IA. In attempting to elucidate this phenomenon we undertook the present investigation to assess the reliability of measuring chemiluminescence (CL) by phagocytizing neutrophils as a tool in defining the opsonic requirement of P. acnes. Neutrophils (PMN), purified through Methocel-isopaque, were mixed with P. acnes or P. granulosum in the presence of pooled human serum; Mg-EGTA chelated serum; and heat inactivated serum. The results demonstrate that CL intensity is proportionally related to serum concentration, number of PMNs, and bacteria and PMN ratios. Complement was required for efficient opsonization. Additional data reveal that there are qualitative and quantitative differences in the requirements for optimal ingestion of P. acnes and P. granulosum. Our data suggest that whereas both the classical and alternative pathways are operative for opsonization of P. acnes, the alternative pathway provides opsonic activity equal to that of the total complement pathway for P. granulosum. We conclude that CL is an useful screening method for detecting impaired opsonization in patients with IA and for monitoring immune response to acne therapy in a very precise manner.