Phase tunable nickel doped Mn3O4 nanoparticle synthesis by chemical precipitation: kinetic study on dye degradation.

Nickel (Ni) doped Mn3O4 nanoparticles (NPs) were synthesized by a quick and facile chemical precipitation technique to investigate their performance in the degradation of methylene blue (MB) in the absence of light. XRD, FESEM, TEM, AAS, XPS, and FT-IR were used for the investigation of the structural, surface morphological, and elemental composition of Ni doped Mn3O4 NPs. XRD confirms the formation of a tetragonal phase structure of pure Mn3O4 and 1% and 3% Ni doped Mn3O4 NPs. However, mixed phases were found in the case of 5 to 10% Ni doped Mn3O4 NPs. Well-defined spherical-shaped morphology was presented through FESEM. Particle sizes decreased linearly (58.50 to 23.68 nm) upon increasing the doping concentration from 0% (pure Mn3O4) to 7% respectively, and then increased (48.62 nm) in the case of 10% doping concentration. TEM further confirmed spherical shaped 32 nm nanoparticles for 7% Ni doped Mn3O4. The elemental composition and oxidation state of the prepared NPs were confirmed by using XPS spectra. Mixed valence Mn2+ and Mn4+ states were found in pure Mn3O4 and 1% and 3% Ni doped Mn3O4 NPs in the ratio of 2MnO-MnO2. In addition, three different oxidation states Mn2+, Mn3+, and Mn4+ were found in 5 to 10% Ni doped Mn3O4 NPs. Moreover, as a dopant Ni exists as Ni2+ and Ni3+ states in all Ni doped Mn3O4 NPs. The synthesized NPs were then applied as potent oxidants for the degradation of MB at pH 3. With the increase of doping concentration to 7%, the degree of degradation was increased to 79% in the first 10 min and finally, it became about 98%. The degradation of MB follows the pseudo-first-order linear kinetics with a degradation rate of 0.0342 min-1.

Transcriptomic Profiling of Influenza A Virus-Infected Mouse Lung at Recovery Stage Using RNA Sequencing.

Influenza A virus (IAV) is known to cause mild to severe respiratory illness. Under some conditions, the infection can lead to pneumonia (viral or bacterial), acute respiratory distress syndrome, and other complications that can be fatal, especially in vulnerable populations such as the elderly, young children, and individuals with underlying health conditions. Despite previous studies, little is known about the host immune response and neuroimmune interactions in IAV infection. Using RNA sequencing, we performed transcriptomic analysis of murine lung tissue 21 days post infection (dpi) with IAV (H1N1) in order to find the differentially expression genes (DEGs) related to the host immune response and neuroimmune interactions inside the lung during recovery. Among 792 DEGs, 434 genes were up-regulated, whereas 358 genes were down-regulated. The most prominent molecular functions of the up-regulated genes were related to the immune response and tissue repair, whereas a large proportion of the down-regulated genes were associated with neural functions. Although further molecular/functional studies need to be performed for these DEGs, our results facilitate the understanding of the host response (from innate immunity to adaptive immunity) and neuroimmune interactions in infected lungs at the recovery stage of IAV infection. These genes might have potential uses as mechanistic/diagnostic biomarkers and represent possible targets for anti-IAV therapies.

Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches.

The SARS-CoV-2 3CLpro protein is one of the key therapeutic targets of interest for COVID-19 due to its critical role in viral replication, various high-quality protein crystal structures, and as a basis for computationally screening for compounds with improved inhibitory activity, bioavailability, and ADMETox properties. The ChEMBL and PubChem database contains experimental data from screening small molecules against SARS-CoV-2 3CLpro, which expands the opportunity to learn the pattern and design a computational model that can predict the potency of any drug compound against coronavirus before in-vitro and in-vivo testing. In this study, Utilizing several descriptors, we evaluated 27 machine learning classifiers. We also developed a neural network model that can correctly identify bioactive and inactive chemicals with 91% accuracy, on CheMBL data and 93% accuracy on combined data on both CheMBL and Pubchem. The F1-score for inactive and active compounds was 93% and 94%, respectively. SHAP (SHapley Additive exPlanations) on XGB classifier to find important fingerprints from the PaDEL descriptors for this task. The results indicated that the PaDEL descriptors were effective in predicting bioactivity, the proposed neural network design was efficient, and the Explanatory factor through SHAP correctly identified the important fingertips. In addition, we validated the effectiveness of our proposed model using a large dataset encompassing over 100,000 molecules. This research employed various molecular descriptors to discover the optimal one for this task. To evaluate the effectiveness of these possible medications against SARS-CoV-2, more in-vitro and in-vivo research is required.

Global Burden of Lumpy Skin Disease, Outbreaks, and Future Challenges.

Lumpy skin disease (LSD), a current global concern, causes economic devastation in livestock industries, with cattle and water buffalo reported to have higher morbidity and lower mortality rates. LSD is caused by lumpy skin disease virus (LSDV), a member of the Poxviridae family. It is an enzootic, rapidly explorative and sometimes fatal infection, characterized by multiple raised nodules on the skin of infected animals. It was first reported in Zambia in 1929 and is considered endemic in Africa south of the Sahara desert. It has gradually spread beyond Africa into the Middle East, with periodic occurrences in Asian and East European countries. Recently, it has been spreading in most Asian countries including far East Asia and threatens incursion to LSD-free countries. Rapid and accurate diagnostic capabilities, virus identification, vaccine development, vector control, regional and international collaborations and effective biosecurity policies are important for the control, prevention, and eradication of LSD infections. This review critically evaluates the global burden of LSD, the chronological historical outbreaks of LSD, and future directions for collaborative global actions.

Evaluating the Brexit and COVID-19's influence on the UK economy: A data analysis.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country's robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK's four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK's total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

YoNet: A Neural Network for Yoga Pose Classification.

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model's performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision.

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

Facile acid fermentation extraction of silkworm pupae oil and evaluation of its physical and chemical properties for utilization as edible oil.

Considering the increasing demand for edible oil in recent times, their price in the world market is becoming skyrocketing. In this research, we produced cost-effective edible oil from desilked silkworm pupae (Bombyx mori) applying a facile acid fermentation process, for the first time. The extraction was performed using two different types of organic acids, 3% of each acetic and citric acid. The yield of the extracted oil was 3.52 ± 0.23% from fresh silkworm pupae. The produced oil was then characterized physically and chemically to know its suitability to be used as edible oil. The oil was found with a low peroxide and acid value of 4.82 meq/kg and 1.35 mg KOH/g oil, respectively, and comprised of different fatty acids, in which palmitic acid (32.04%) and oleic acid (34.62%) were in large portions among the total fatty acids. Additionally, the extracted oil included linoleic, α-linolenic, and dihomo-gamma-linolenic acid which have health benefits. The oil was rich with minerals such as Iron, Sodium, Potassium, Calcium, Magnesium, Zinc, and Phosphorus with a negligible concentration of toxic elements such as Manganese, Cobalt, Nickel, Copper, Lead, Cadmium, Chromium, Arsenic, and Silver, indicating a good nutritive value of the extracted oil. Overall, the outcomes of all the characterizations showed that the extracted oil could be used as good edible oil and the corresponding acid fermentation extraction process has the potential to be used as an effective oil extraction method for silkworm pupae.

Automated water recycle (AWR) method for dust removal from rooftop photovoltaic (PV) at Johor, Malaysia.

Abstract: Wet dust on the Photovoltaic (PV) surface is a persistent problem that is merely considered for rooftop based PV cleaning under a high humid climate like Malaysia. This paper proposes an Automated Water Recycle (AWR) method encompassing a water recycling unit for rooftop PV cleaning with the aim to enhance the electrical performance. This study makes a major contribution by developing a new model to correlate output power ( P out ) and dust-fall factor. For model validation, we conducted an experiment of taking one set of Monocrystalline PV (mono) on a 340 W m 2 of medium luminance day. One mono module was cleaned by AWR - pressurized water sprayed through 11 small holes over its front surface, while the other module was left with no-cleaning. The dust-contaminated water was filtered and collected back to the tank for recycling process. The water loss per cleaning cycle was achieved 0.32%, which was normalized to net loss of 28.8% at a frequency of 1 cycle/day for 90 days of operation. We observed that P out of no-cleaning PV was decreased by 29.44% than that of AWR method. From this experimental data also, a unique and more accurate model is created for P out prediction, which is much simpler compared to multivariables equation. Our investigation offers important insights into the accuracy of this regression model demonstrated by R 2 = 0.744 or a strong negative quadratic relationship between P out and dust-fall. The cleaning of PV modules is expected to save significant energy to reduce the payback period. Article Highlights: An automated water recycle method for cleaning dust-fall in rooftop photovoltaic module is proposed.Both simulation and experimental models are developed to predict output power of the photovoltaic module.Proposed method can produce 24.40% more output power than a no-cleaning system with a mere water loss of 0.32%/cycle.

Development of a high-sensitivity and short-duration fluorescence in situ hybridization method for viral mRNA detection in HEK 293T cells.

Coronavirus disease 2019 (COVID-19) is an extremely contagious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early disease recognition of COVID-19 is crucial not only for prompt diagnosis and treatment of the patients, but also for effective public health surveillance and response. The reverse transcription-polymerase chain reaction (RT-PCR) is the most common method for the detection of SARS-CoV-2 viral mRNA and is regarded as the gold standard test for COVID-19. However, this test and those for antibodies (IgM and IgG) and antigens have certain limitations (e.g., by yielding false-negative and false-positive results). We have developed an RNA fluorescence in situ hybridization (FISH) method for high-sensitivity detection of SARS-CoV-2 mRNAs in HEK 293T cell cultures as a model. After transfection of HEK 293T cells with plasmids, Spike (S)/envelope (E) proteins and their mRNAs were clearly detected inside the cells. In addition, hybridization time could be reduced to 2 hours for faster detection when probe concentration was increased. Our approach might thus significantly improve the sensitivity and specificity of SARS-CoV-2 detection and be widely applied for the high-sensitivity single-molecular detection of other RNA viruses (e.g., Middle East respiratory syndrome coronavirus (MERS-CoV), Hepatitis A virus, all influenza viruses, and human immunodeficiency virus (HIV)) in various types of samples including tissue, body fluid, blood, and water. RNA FISH can also be utilized for the detection of DNA viruses (e.g., Monkeypox virus, human papillomavirus (HPV), and cytomegalovirus (CMV)) by detection of their mRNAs inside cells or body fluid.

Controlling Intermolecular Interaction and Interphase Chemistry Enabled Sustainable Water-tolerance LiMn2 O4 ||Li4 Ti5 O12 Batteries.

Solid electrolyte interphase (SEI) formation and H2 O activity reduction in Water-in-Salt electrolytes (WiSE) with an enlarged stability window of 3.0 V have provided the feasibility of the high-energy-density aqueous Li-ion batteries. Here, we extend the cathodic potential of WiSE by rationally controlling intermolecular interaction and interphase chemistry with the introduction of trimethyl phosphate (TMP) into WiSE. The TMP not merely limits the H2 O activity via the strong interaction between TMP and H2 O but also contributes to the formation of reinforced SEI involving phosphate and LiF by manipulating the Li+ solvation structure. Thus, water-tolerance LiMn2 O4 (LMO)||Li4 Ti5 O12 (LTO) full cell with a P/N ratio of 1.14 can be assembled and achieve a long cycling life of 1000 times with high coulombic efficiency of >99.9 %. This work provides a promising insight into the cost-effective practical manufacture of LMO||LTO cells without rigorous moisture-free requirements.

Robust and efficient COVID-19 detection techniques: A machine learning approach.

The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.

Contingent Valuation: A Pilot Study for Eliciting Willingness to Pay for a Reduction in Mortality From Vaccine-Preventable Illnesses for Children and Adults in Bangladesh.

OBJECTIVES: The contingent valuation (CV) method elicits willingness to pay (WTP) for calculating the value of statistical life (VSL). CV approaches for assessing VSL are uncommon in many low and middle-income countries (LMICs). Between 2008 and 2018 only 44 articles utilized WTP in a health-related field and of these only 5 (11%) utilized CV to assess the WTP for a mortality risk reduction. We elicit WTP estimates and compute VSL using the CV method in Bangladesh. METHODS: The pilot study was primarily aimed at developing best practice guidelines for CV studies in LMICs to get more robust WTP estimates. To this end, we explored three methodological a) Varying the name of the intervention, keeping all other characteristics constant; b) varying the effectiveness of the health intervention and c) offering an overnight period to think about the WTP scenario. The survey was administered 413 randomly selected participants. VSL was for a 1/3000 mortality risk reduction. RESULTS: We had more males (54%) than females (46%) and the mean annual self-reported income was $5,683.36. Mean VSL is $11,339.70 with a median of $10,413. The ratio of child: adult WTP is approximately 1 by both gender and age category. The vaccine intervention had the largest amount of $0 WTP and protest responses (52% and 58% respectively). 93% of the participants were able to describe (teach-back) the vaccine effectiveness using their own family as an example. CONCLUSION: Our study provides empirical evidence on how to better generate CV surveys to produce more robust WTP estimates.

How to evaluate the implementation of complex health programmes in low-income settings: the approach of the Gavi Full Country Evaluations.

Vaccination, like most other public health services, relies on a complex package of intervention components, functioning systems and committed actors to achieve universal coverage. Despite significant investment in immunization programmes, national coverage trends have slowed and equity gaps have grown. This paper describes the design and implementation of the Gavi Full Country Evaluations, a multi-country, prospective, mixed-methods approach whose goal was to monitor and evaluate processes, inputs, outputs and outcomes of immunization programmes in Bangladesh, Mozambique, Uganda and Zambia. We implemented the Full Country Evaluations from 2013 to 2018 with the goal of identifying the drivers of immunization programme improvement to support programme implementation and increase equitable immunization coverage. The framework supported methodological and paradigmatic flexibility to respond to a broad range of evaluation and implementation research questions at global, national and cross-country levels, but was primarily underpinned by a focus on evaluating processes and identifying the root causes of implementation breakdowns. Process evaluation was driven by theories of change for each Gavi funding stream (e.g. Health Systems Strengthening) or activity, ranging from global policy development to district-level programme implementation. Mixing of methods increased in relevance and rigour over time as we learned to build multiple methods into increasingly tailored evaluation questions. Evaluation teams in country-based research institutes increasingly strengthened their level of embeddedness with immunization programmes as the emphasis shifted over time to focus more heavily on the use of findings for programme learning and adaptation. Based on our experiences implementing this approach, we recommend it for the evaluation of other complex interventions, health programmes or development assistance.

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer.

Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological disorders, so understanding how to develop this approach for one's area of inquiry is vital. We previously proposed an approach that combined wearable inertial measurement units (IMUs) and ML to classify motions made by stroke patients. However, our approach had computational and practical limitations. We address these limitations here in the form of a primer, presenting how to optimize a sensor-ML approach for clinical implementation. First, we demonstrate how to identify the ML algorithm that maximizes classification performance and pragmatic implementation. Second, we demonstrate how to identify the motion capture approach that maximizes classification performance but reduces cost. We used previously collected motion data from chronic stroke patients wearing off-the-shelf IMUs during a rehabilitation-like activity. To identify the optimal ML algorithm, we compared the classification performance, computational complexity, and tuning requirements of four off-the-shelf algorithms. To identify the optimal motion capture approach, we compared the classification performance of various sensor configurations (number and location on the body) and sensor type (IMUs vs. accelerometers). Of the algorithms tested, linear discriminant analysis had the highest classification performance, low computational complexity, and modest tuning requirements. Of the sensor configurations tested, seven sensors on the paretic arm and trunk led to the highest classification performance, and IMUs outperformed accelerometers. Overall, we present a refined sensor-ML approach that maximizes both classification performance and pragmatic implementation. In addition, with this primer, we showcase important considerations for appraising off-the-shelf algorithms and sensors for quantitative motion assessment.

Rechargeable-battery chemistry based on lithium oxide growth through nitrate anion redox.

Next-generation lithium-battery cathodes often involve the growth of lithium-rich phases, which enable specific capacities that are 2-3 times higher than insertion cathode materials, such as lithium cobalt oxide. Here, we investigated battery chemistry previously deemed irreversible in which lithium oxide, a lithium-rich phase, grows through the reduction of the nitrate anion in a lithium nitrate-based molten salt at 150 °C. Using a suite of independent characterization techniques, we demonstrated that a Ni nanoparticle catalyst enables the reversible growth and dissolution of micrometre-sized lithium oxide crystals through the effective catalysis of nitrate reduction and nitrite oxidation, which results in high cathode areal capacities (~12 mAh cm-2). These results enable a rechargeable battery system that has a full-cell theoretical specific energy of 1,579 Wh kg-1, in which a molten nitrate salt serves as both an active material and the electrolyte.

A Taxonomy of Functional Upper Extremity Motion.

Background: Functional upper extremity (UE) motion enables humans to execute activities of daily living (ADLs). There currently exists no universal language to systematically characterize this type of motion or its fundamental building blocks, called functional primitives. Without a standardized classification approach, pooling mechanistic knowledge and unpacking rehabilitation content will remain challenging. Methods: We created a taxonomy to characterize functional UE motions occurring during ADLs, classifying them by motion presence, temporal cyclicity, upper body effector, and contact type. We identified five functional primitives by their phenotype and purpose: reach, reposition, transport, stabilize, and idle. The taxonomy was assessed for its validity and interrater reliability in right-paretic chronic stroke patients performing a selection of ADL tasks. We applied the taxonomy to identify the primitive content and motion characteristics of these tasks, and to evaluate the influence of impairment level on these outcomes. Results: The taxonomy could account for all motions in the sampled activities. Interrater reliability was high for primitive identification (Cohen's kappa = 0.95-0.99). Using the taxonomy, the ADL tasks were found to be composed primarily of transport and stabilize primitives mainly executed with discrete, proximal motions. Compared to mildly impaired patients, moderately impaired patients used more repeated reaches and axial-proximal UE motion to execute the tasks. Conclusions: The proposed taxonomy yields objective, quantitative data on human functional UE motion. This new method could facilitate the decomposition and quantification of UE rehabilitation, the characterization of functional abnormality after stroke, and the mechanistic examination of shared behavior in motor studies.

Differential Poststroke Motor Recovery in an Arm Versus Hand Muscle in the Absence of Motor Evoked Potentials.

Background. After stroke, recovery of movement in proximal and distal upper extremity (UE) muscles appears to follow different time courses, suggesting differences in their neural substrates. Objective. We sought to determine if presence or absence of motor evoked potentials (MEPs) differentially influences recovery of volitional contraction and strength in an arm muscle versus an intrinsic hand muscle. We also related MEP status to recovery of proximal and distal interjoint coordination and movement fractionation, as measured by the Fugl-Meyer Assessment (FMA). Methods. In 45 subjects in the year following ischemic stroke, we tracked the relationship between corticospinal tract (CST) integrity and behavioral recovery in the biceps (BIC) and first dorsal interosseous (FDI) muscle. We used transcranial magnetic stimulation to probe CST integrity, indicated by MEPs, in BIC and FDI. We used electromyography, dynamometry, and UE FMA subscores to assess muscle-specific contraction, strength, and inter-joint coordination, respectively. Results. Presence of MEPs resulted in higher likelihood of muscle contraction, greater strength, and higher FMA scores. Without MEPs, BICs could more often volitionally contract, were less weak, and had steeper strength recovery curves than FDIs; in contrast, FMA recovery curves plateaued below normal levels for both the arm and hand. Conclusions. There are shared and separate substrates for paretic UE recovery. CST integrity is necessary for interjoint coordination in both segments and for overall recovery. In its absence, alternative pathways may assist recovery of volitional contraction and strength, particularly in BIC. These findings suggest that more targeted approaches might be needed to optimize UE recovery.

Deoxynucleoside Therapy for Thymidine Kinase 2-Deficient Myopathy.

OBJECTIVE: Thymidine kinase 2, encoded by the nuclear gene TK2, is required for mitochondrial DNA maintenance. Autosomal recessive TK2 mutations cause depletion and multiple deletions of mtDNA that manifest predominantly as a myopathy usually beginning in childhood and progressing relentlessly. We investigated the safety and efficacy of deoxynucleoside monophosphate and deoxynucleoside therapies. METHODS: We administered deoxynucleoside monophosphates and deoxynucleoside to 16 TK2-deficient patients under a compassionate use program. RESULTS: In 5 patients with early onset and severe disease, survival and motor functions were better than historically untreated patients. In 11 childhood and adult onset patients, clinical measures stabilized or improved. Three of 8 patients who were nonambulatory at baseline gained the ability to walk on therapy; 4 of 5 patients who required enteric nutrition were able to discontinue feeding tube use; and 1 of 9 patients who required mechanical ventilation became able to breathe independently. In motor functional scales, improvements were observed in the 6-minute walk test performance in 7 of 8 subjects, Egen Klassifikation in 2 of 3, and North Star Ambulatory Assessment in all 5 tested. Baseline elevated serum growth differentiation factor 15 levels decreased with treatment in all 7 patients tested. A side effect observed in 8 of the 16 patients was dose-dependent diarrhea, which did not require withdrawal of treatment. Among 12 other TK2 patients treated with deoxynucleoside, 2 adults developed elevated liver enzymes that normalized following discontinuation of therapy. INTERPRETATION: This open-label study indicates favorable side effect profiles and clinical efficacy of deoxynucleoside monophosphate and deoxynucleoside therapies for TK2 deficiency. ANN NEUROL 2019;86:293-303.

Assessing the readiness of health facilities for diabetes and cardiovascular services in Bangladesh: a cross-sectional survey.

OBJECTIVE: The objective of this study was to assess the readiness of health facilities for diabetes and cardiovascular services in Bangladesh. DESIGN: This study was a cross-sectional survey. SETTING: This study used data from a nationwide Bangladesh Health Facility Survey conducted by the Ministry of Health and Social Welfare in 2014. PARTICIPANTS: A total of 319 health facilities delivering services focused on diabetes and cardiovascular diseases (CVD) were included in the survey. Some of these facilities were run by the public sector while others were managed by the private sector and non-governmental organisations. It was a mix of primary and secondary care facilities. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was readiness of health facilities for diabetes and cardiovascular services. We analysed relevant data following the Service Availability and Readiness Assessment manual of the WHO to assess the readiness of selected health facilities towards services for diabetes and CVD. RESULTS: 58% and 24.1% of the facilities had diagnosis and treatment services for diabetes and CVD, respectively. Shortage of trained staff (18.8% and 14.7%) and lack of adequate medicine supply (23.5% and 43.9%) were identified to be factors responsible for inadequate services for diabetes and CVD. Among the facilities that offer services for diabetes and CVD, only 0.4% and 0.9% had all the four service readiness factors (guideline, trained staff, equipment and medicine). CONCLUSIONS: The study suggests that health facilities suffered from numerous drawbacks, such as shortage of trained staff and required medicine. Most importantly, they lack effective guidelines on the diagnosis and treatment for diabetes and CVD. It is, therefore, essential now to ensure that there are trained staff, adequate medicine supply, and appropriate guidelines on the diagnosis and treatment for diabetes and CVD in Bangladesh.