COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems.

Polycyclic aromatic systems are highly important to numerous applications, in particular to organic electronics and optoelectronics. High-throughput screening and generative models that can help to identify new molecules to advance these technologies require large amounts of high-quality data, which is expensive to generate. In this report, we present the largest freely available dataset of geometries and properties of cata-condensed poly(hetero)cyclic aromatic molecules calculated to date. Our dataset contains ~500k molecules comprising 11 types of aromatic and antiaromatic building blocks calculated at the GFN1-xTB level and is representative of a highly diverse chemical space. We detail the structure enumeration process and the methods used to provide various electronic properties (including HOMO-LUMO gap, adiabatic ionization potential, and adiabatic electron affinity). Additionally, we benchmark against a ~50k dataset calculated at the CAM-B3LYP-D3BJ/def2-SVP level and develop a fitting scheme to correct the xTB values to higher accuracy. These new datasets represent the second installment in the COMputational database of Polycyclic Aromatic Systems (COMPAS) Project.

Stereo-electronic factors influencing the stability of hydroperoxyalkyl radicals: transferability of chemical trends across hydrocarbons and ab initio methods.

The hydroperoxyalkyl radicals (˙QOOH) are known to play a significant role in combustion and tropospheric processes, yet their direct spectroscopic detection remains challenging. In this study, we investigate molecular stereo-electronic effects influencing the kinetic and thermodynamic stability of a ˙QOOH along its formation path from the precursor, alkylperoxyl radical (ROO˙), and the depletion path resulting in the formation of cyclic ether + ˙OH. We focus on reactive intermediates encountered in the oxidation of acyclic hydrocarbon radicals: ethyl, isopropyl, isobutyl, tert-butyl, neopentyl, and their alicyclic counterparts: cyclohexyl, cyclohexenyl, and cyclohexadienyl. We report reaction energies and barriers calculated with the highly accurate method Weizmann-1 (W1) for the channels: ROO˙ ⇌ ˙QOOH, ROO˙ ⇌ alkene + ˙OOH, ˙QOOH ⇌ alkene + ˙OOH, and ˙QOOH ⇌ cyclic ether + ˙OH. Using W1 results as a reference, we have systematically benchmarked the accuracy of popular density functional theory (DFT), composite thermochemistry methods, and an explicitly correlated coupled-cluster method. We ascertain inductive, resonance, and steric effects on the overall stability of ˙QOOH and computationally investigate the possibility of forming more stable species. With new reactions as test cases, we probe the capacity of various ab initio methods to yield quantitative insights on the elementary steps of combustion.

Understanding the Role of Intramolecular Ion-Pair Interactions in Conformational Stability Using an Ab Initio Thermodynamic Cycle.

Intramolecular ion-pair interactions yield shape and functionality to many molecules. With proper orientation, these interactions overcome steric factors and are responsible for the compact structures of several peptides. In this study, we present a thermodynamic cycle based on isoelectronic and alchemical mutation to estimate the intramolecular ion-pair interaction energy. We determine these energies for 26 benchmark molecules with common ion-pair combinations and compare them with results obtained using intramolecular symmetry-adapted perturbation theory. For systems with long linkers, the ion-pair energies evaluated using both approaches deviate by less than 2.5% in the vacuum phase. The thermodynamic cycle based on density functional theory facilitates calculations of salt-bridge interactions in model tripeptides with continuum/microsolvation modeling and four large peptides: 1EJG (crambin), 1BDK (bradykinin), 1L2Y (a mini-protein with a tryptophan cage), and 1SCO (a toxin from the scorpion venom).

Guided diffusion for inverse molecular design.

The holy grail of materials science is de novo molecular design, meaning engineering molecules with desired characteristics. The introduction of generative deep learning has greatly advanced efforts in this direction, yet molecular discovery remains challenging and often inefficient. Herein we introduce GaUDI, a guided diffusion model for inverse molecular design that combines an equivariant graph neural net for property prediction and a generative diffusion model. We demonstrate GaUDI's effectiveness in designing molecules for organic electronic applications by using single- and multiple-objective tasks applied to a generated dataset of 475,000 polycyclic aromatic systems. GaUDI shows improved conditional design, generating molecules with optimal properties and even going beyond the original distribution to suggest better molecules than those in the dataset. In addition to point-wise targets, GaUDI can also be guided toward open-ended targets (for example, a minimum or maximum) and in all cases achieves close to 100% validity of generated molecules.

Anomaly Detection Based on Time Series Data of Hydraulic Accumulator.

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set.

Structure-based discovery of (S)-2-amino-6-(4-fluorobenzyl)-5,6,11,11a-tetrahydro-1H-imidazo[1',5':1,6]pyrido[3,4-b]indole-1,3(2H)-dione as low nanomolar, orally bioavailable autotaxin inhibitor.

Inhibition of extracellular secreted enzyme autotaxin (ATX) represents an attractive strategy for the development of new therapeutics to treat various diseases and a few inhibitors entered in clinical trials. We herein describe structure-based design, synthesis, and biological investigations revealing a potent and orally bioavailable ATX inhibitor 1. During the molecular docking and scoring studies within the ATX enzyme (PDB-ID: 4ZGA), the S-enantiomer (Gscore = -13.168 kcal/mol) of the bound ligand PAT-494 scored better than its R-enantiomer (Gscore = -9.562 kcal/mol) which corroborated with the reported observation and analysis of the results suggested the scope of manipulation of the hydantoin substructure in PAT-494. Accordingly, the docking-based screening of a focused library of 10 compounds resulted in compound 1 as a better candidate for pharmacological studies. Compound 1 was synthesized from L-tryptophan and evaluated against ATX enzymatic activities with an IC50 of 7.6 and 24.6 nM in biochemical and functional assays, respectively. Further, ADME-PK studies divulged compound 1 as non-cytotoxic (19.02% cell growth inhibition at 20 µM in human embryonic kidney cells), metabolically stable against human liver microsomes (CLint  = 15.6 µl/min/mg; T1/2  = 113.2 min) with solubility of 4.82 µM and orally bioavailable, demonstrating its potential to be used for in vivo experiments.

Troubleshooting unstable molecules in chemical space.

A key challenge in automated chemical compound space explorations is ensuring veracity in minimum energy geometries-to preserve intended bonding connectivities. We discuss an iterative high-throughput workflow for connectivity preserving geometry optimizations exploiting the nearness between quantum mechanical models. The methodology is benchmarked on the QM9 dataset comprising DFT-level properties of 133 885 small molecules, wherein 3054 have questionable geometric stability. Of these, we successfully troubleshoot 2988 molecules while maintaining a bijective mapping with the Lewis formulae. Our workflow, based on DFT and post-DFT methods, identifies 66 molecules as unstable; 52 contain -NNO-, and the rest are strained due to pyramidal sp2 C. In the curated dataset, we inspect molecules with long C-C bonds and identify ultralong candidates (r > 1.70 Å) supported by topological analysis of electron density. The proposed strategy can aid in minimizing unintended structural rearrangements during quantum chemistry big data generation.

Critical benchmarking of popular composite thermochemistry models and density functional approximations on a probabilistically pruned benchmark dataset of formation enthalpies.

First-principles calculation of the standard formation enthalpy, ΔHf° (298 K), in such a large scale as required by chemical space explorations, is amenable only with density functional approximations (DFAs) and certain composite wave function theories (cWFTs). Unfortunately, the accuracies of popular range-separated hybrid, "rung-4" DFAs, and cWFTs that offer the best accuracy-vs-cost trade-off have until now been established only for datasets predominantly comprising small molecules; their transferability to larger systems remains vague. In this study, we present an extended benchmark dataset of ΔHf° for structurally and electronically diverse molecules. We apply quartile-ranking based on boundary-corrected kernel density estimation to filter outliers and arrive at probabilistically pruned enthalpies of 1694 compounds (PPE1694). For this dataset, we rank the prediction accuracies of G4, G4(MP2), ccCA, CBS-QB3, and 23 popular DFAs using conventional and probabilistic error metrics. We discuss systematic prediction errors and highlight the role an empirical higher-level correction plays in the G4(MP2) model. Furthermore, we comment on uncertainties associated with the reference empirical data for atoms and the systematic errors stemming from these that grow with the molecular size. We believe that these findings will aid in identifying meaningful application domains for quantum thermochemical methods.

Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design.

Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV, combinations of functional groups and substitution sites can shift the peak position by ±1 eV. Time-dependent long-range corrected hybrid density functional methods can model the lowest excitation energies offering a semi-quantitative precision of ±0.3 eV. Alas, the chemical space of BODIPYs stemming from combinatorial introduction of-even a few dozen-substituents is too large for brute-force high-throughput modeling. To navigate this vast space, we select 77 412 molecules and train a kernel-based quantum machine learning model providing <2% hold-out error. Further reuse of the results presented here to navigate the entire BODIPY universe comprising over 253 giga (253 × 109) molecules is demonstrated by inverse-designing candidates with desired target excitation energies.

Findings from the 2017 HIV estimation round & trend analysis of key indicators 2010-2017: Evidence for prioritising HIV/AIDS programme in India.

Background & objectives: The National AIDS Control Organisation (NACO) and the ICMR-National Institute of Medical Statistics, the nodal agency for conducting HIV estimations in India, have been generating HIV estimates regularly since 2003. The objective of this study was to describe India's biennial HIV estimation 2017 process, data inputs, tool, methodology and epidemiological assumptions used to generate the HIV estimates and trends of key indicators for 2010-2017 at national and State/Union Territory levels. Methods: Demographic Projection (DemProj) and AIDS Impact Modules (AIM) of Spectrum 5.63 software recommended by the United Nations Programme on HIV and AIDS Global Reference Group on HIV Estimates, Modelling and Projections, were used for generating HIV estimations on key indicators. HIV sentinel surveillance, epidemiological and programme data were entered into Estimation Projection Package (EPP), and curve fitting was done using EPP classic model. Finally, calibration was done using the State HIV prevalence of two rounds of National Family Health Survey (NFHS) -3 and -4 and Integrated Biological and Behavioural Surveillance (IBBS), 2014-2015. Results: The national adult prevalence of HIV was estimated to be 0.22 per cent in 2017. Mizoram, Manipur and Nagaland had the highest prevalence over one per cent. An estimated 2.1 million people were living with HIV in 2017, with Maharashtra estimated to have the highest number. Of the 88 thousand annual new HIV infections estimated nationally in 2017, Telangana accounted for the largest share. HIV incidence was found to be higher among key population groups, especially people who inject drugs. The annual AIDS-related deaths were estimated to be 69 thousand nationally. For all indicators, geographic variation in levels and trends between States existed. Interpretation & conclusions: With a slow decline in annual new HIV infections by only 27 per cent from 2010 to 2017 against the national target of 75 per cent by 2020, the national target to end AIDS by 2030 may be missed; although at the sub-national level some States have made better progress to reduce new HIV infection. It calls for reinforcement of HIV prevention, diagnosis and treatment efforts by geographical regions and population groups.

A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson's Disease Using Wearable Based Gait Signals.

Fluctuations in motor symptoms are mostly observed in Parkinson's disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the "On"/"Off" state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.

Detection of Parkinson's Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network.

Parkinson's Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time. Additionally, as there is an exponential growth of the aging population in the world the number of people suffering from Parkinson's Disease is increasing and it levies a huge economic burden on governments. However, until now no therapeutic method has been discovered for completely eradicating the disease from a person's body after it's onset. Therefore, the early detection of Parkinson's Disease is of paramount importance to tackle the progressive loss of dopaminergic neurons in patients to serve them with a better life. In this study, 3T T1-weighted MRI scans were acquired from the Parkinson's Progression Markers Initiative (PPMI) database of 406 subjects from baseline visit, where 203 were healthy and 203 were suffering from Parkinson's Disease. Following data pre-processing, a 3D convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Parkinson's Disease. In the end, it was observed that the developed 3D CNN model performed superiorly by completely aligning with the hypothesis of the study and plotted an overall accuracy of 95.29%, average recall of 0.943, average precision of 0.927, average specificity of 0.9430, f1-score of 0.936, and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) score of 0.98 for both the classes respectively.

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients.

In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.

3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson's Disease Using Artificial Neural Networks.

Parkinson's disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson's disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson's disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson's disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson's disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices.

Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, each individual now expects to be delivered with state-of-the-art reports about his body. The most dominant wearables in the market often focus on general metrics such as the number of steps, distance walked, heart rate, oximetry, sleep quality, and sleep stage. But, for accurately identifying the well-being of an individual, another important metric needs to be analyzed, which is the state of mind. The state of mind is a metric of an individual that boils down to the activity of all other related metrics. But, the detection of the state of mind has formed a huge challenge for the researchers as a single biosignal cannot propose a particular decision threshold for detection. Therefore, in this work, multiple biosignals from different parts of the body are used to determine the state of mind of an individual. The biosignals, blood volume pulse (BVP), and accelerometer are intercepted from a wrist-worn wearable, and electrocardiography (ECG), electromyography (EMG), and respiration are intercepted from a chest-worn pod. For the classification of the biosignals to the multiple state-of-mind categories, a multichannel convolutional neural network architecture was developed. The overall model performed pretty well and pursued some encouraging results by demonstrating an average recall and precision of 97.238% and 97.652% across all the classes, respectively.

The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling.

Combinatorial introduction of heteroatoms in the two-dimensional framework of aromatic hydrocarbons opens up possibilities to design compound libraries exhibiting desirable photovoltaic and photochemical properties. Exhaustive enumeration and first-principles characterization of this chemical space provide indispensable insights for rational compound design strategies. Here, for the smallest seventy-seven Kekulean-benzenoid polycyclic systems, we reveal combinatorial substitution of C atom pairs with the isosteric and isoelectronic B, N pairs to result in 7 453 041 547 842 (7.4 tera) unique molecules. We present comprehensive frequency distributions of this chemical space, analyze trends, and discuss a symmetry-controlled selectivity manifestable in synthesis product yield. Furthermore, by performing high-throughput ab initio density functional theory calculations of over thirty-three thousand (33k) representative molecules, we discuss quantitative trends in the structural stability and inter-property relationships across heteroarenes. Our results indicate a significant fraction of the 33k molecules to be electronically active in the 1.5-2.5 eV region, encompassing the most intense region of the solar spectrum, indicating their suitability as potential light-harvesting molecular components in photo-catalyzed solar cells.

Antinociceptive properties of shikonin: in vitro and in vivo studies.

Shikonin possess a diverse spectrum of pharmacological properties in multiple therapeutic areas. However, the nociceptive effect of shikonin is not largely known. To investigate the antinociceptive potential of shikonin, panel of GPCRs, ion channels, and enzymes involved in pain pathogenesis were studied. To evaluate the translation of shikonin efficacy in vivo, it was tested in 3 established rat pain models. Our study reveals that shikonin has significant inhibitory effect on pan sodium channel/N1E115 and NaV1.7 channel with half maximal inhibitory concentration (IC50) value of 7.6 µmol/L and 6.4 µmol/L, respectively, in a cell-based assay. Shikonin exerted significant dose dependent antinociceptive activity at doses of 0.08%, 0.05%, and 0.02% w/v in pinch pain model. In mechanical hyperalgesia model, dose of 10 and 3 mg/kg (intraperitoneal) produced dose-dependent analgesia and showed 67% and 35% reversal of hyperalgesia respectively at 0.5 h. Following oral administration, it showed 39% reversal at 30 mg/kg dose. When tested in first phase of formalin induced pain, shikonin at 10 mg/kg dose inhibited paw flinching by ∼71%. In all studied preclinical models, analgesic effect was similar or better than standard analgesic drugs. The present study unveils the mechanistic role of shikonin on pain modulation, predominantly via sodium channel modulation, suggesting that shikonin could be developed as a potential pain blocker.

Efficacy of a new model for delivering integrated TB and HIV services for people living with HIV/AIDS in Delhi -- case for a paradigm shift in national HIV/TB cross-referral strategy.

Under National TB/HIV framework, all TB patients are referred by Revised National Tuberculosis Programme (RNTCP) service providers to Integrated Counseling and Testing Centers (ICTCs) for voluntary counseling and testing (C&T) and ICTC "TB-suspects" are referred to RNTCP facilities for TB diagnosis and treatment. HIV-TB coinfected patients are then referred to Anti Retroviral Treatment (ART) center for initiation of ART between two weeks and two months of initiating TB treatment. During the third phase of National AIDS Control Programme (NACP-III, April 2007-April 2012), 30749/130503 (23.6%) TB/HIV cross-referrals were lost to follow up (LTFU) and there was missed opportunity for 940/1884 (49.9%) HIV-TB coinfected patients for initiation of ART during TB treatment. This motivated Delhi State AIDS Control Society (DSACS) and State TB Cell (STC) to revise existing cross-referral strategy. The new strategy was launched in May 2012, wherein HIV-TB coinfected and HIV-positive "TB-suspects" were referred to nearest ART center for HIV care and investigations of TB at Chest Clinic/Designated Microscopy Centre (DMC) located within the same hospital instead of referral to area RNTCP facility. Outcome of the strategy was evaluated in March 2013. The new HIV-TB cross-referral strategy in Delhi has shown advantage over national strategy: first, improved retention of coinfected clients in HIV care; second, ensured timely initiation of TB-treatment and ART; and third, significantly improved survival of HIV-TB coinfected patients.

Early diagnosis of HIV in children below 18 months using DNA PCR test--assessment of the effectiveness of PMTCT interventions and challenges in early initiation of ART in a resource-limited setting.

This study was conducted to assess efficacy of the current Indian Prevention of Mother-to-Child Transmission (PMTCT) protocol in 217 HIV-exposed infants, and to assess challenges in the early initiation of antiretroviral treatment (ART) in 18 (8.3%) infants with HIV, as determined by the HIV-1 DNA polymerase chain reaction (PCR) test at ≥ 6 weeks to <18 months of age. The mother-to-child transmission (MTCT) rate in 154 mother-baby pairs fully compliant with the PMTCT protocol was 5.2%. However, if 25 pairs who were positive using dried blood spot (DBS) DNA PCR and who did not undergo whole blood testing are included in the analysis, then the overall MTCT rate would be 19.8%. The current protocol is 50% effective considering an MTCT rate of 35-40% without any intervention. ART was initiated in 10 (55.6%) HIV-infected children at a mean ± standard deviation (SD) age of 10.45 ± 4.9 (range: 4-17.5) months; delay resulted in opportunistic infections in one-third of the children. A single-dose nevirapine PMTCT regimen should be replaced by a triple antiretroviral regimen; DBS DNA PCR-positive infants may be given ART, and simultaneously a whole blood specimen should be taken to determine whether ART should be continued.