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Hornworts, one of the three bryophyte phyla, show some of the deepest divergences in extant land plants, with some families separated by more than 300 million years. Previous hornwort genomes represented only one genus, limiting the ability to infer evolution within hornworts and their early land plant ancestors. Here we report ten new chromosome-scale genomes representing all hornwort families and most of the genera. We found that, despite the deep divergence, synteny was surprisingly conserved across all hornwort genomes, a pattern that might be related to the absence of whole-genome duplication. We further uncovered multiple accessory and putative sex chromosomes that are highly repetitive and CpG methylated. In contrast to autosomes, these chromosomes mostly lack syntenic relationships with one another and are evolutionarily labile. Notable gene retention and losses were identified, including those responsible for flavonoid biosynthesis, stomata patterning and phytohormone reception, which have implications in reconstructing the evolution of early land plants. Together, our pan-phylum genomes revealed an array of conserved and divergent genomic features in hornworts, highlighting the uniqueness of this deeply diverged lineage.

Neuromorphic photonic processors are redefining the boundaries of classical computing by enabling high-speed multidimensional information processing within the memory. Memristors, the backbone of neuromorphic processors, retain their state after programming without static power consumption. Among them, electro-optic memristors are of great interest, as they enable dual electrical–optical functionality that bridges the efficiency of electronics and the bandwidth of photonics. However, efficient, scalable, and CMOS-compatible implementations of electro-optic memristors are still lacking. Here, we devise electro-optic memristors by structuring the phase-change material as a nanoscale constriction, geometrically confining the electrically generated heat profile to overlap with the optical field, thus achieving programmability and readability in both the electrical and optical domains. We demonstrate sub-10 pJ electrical switching energy and a high electro-optical modulation efficiency of 0.15 nJ/dB. Our work opens up opportunities for high-performance and energy-efficient integrated electro-optic neuromorphic computing.

AbstractAntibiotic resistance against Mycobacterium tuberculosis(M.tb.) has been a significant cause of death worldwide. The Enhanced intracellular survival (EIS) protein of the bacteria is an acetyltransferase that multiacetylates aminoglycoside antibiotics, preventing them from binding to the bacterial ribosome. To overcome the EIS-mediated antibiotics resistance of M.tb., we compiled 888 alkaloids and derivatives from five different databases and virtually screened them against the EIS receptor. The compound library was filtered down to 87 compounds, which underwent additional analysis and filtration. Moreover, the top 15 most prominent phytocompounds were obtained after the drug-likeness prediction and ADMET screening. Out of 15, nine compounds confirmed the maximum number of hydrogen bond interactions and reliable binding energies during molecular docking. Additionally, the Molecular dynamics (MD) simulation of nine compounds showed the three most stable complexes, further verified by re-docking with mutated protein. The density functional theory (DFT) calculation was performed to identify the HOMO-LUMO energy gaps of the selected three potential compounds. Finally, our selected top lead compounds i.e., Alkaloid AQC2 (PubChem85634496), Nobilisitine A (ChEbi68116), and N-methylcheilanthifoline (ChEbi140673) demonstrated more favourable outcomes when compared with reference compounds (i.e., 39b and 2i) in all parameters used in this study. Therefore, we anticipate that our findings will help to explore and develop natural compound therapy against multi and extensively drug-resistant strains of M.tb.Communicated by Ramaswamy H. Sarma

The current investigation addresses the pressing need to integrate orphan or underutilized crops into mainstream agriculture, focusing on Perilla (Perilla frutescensL.) due to its superior nutritional profile. A major challenge is the lack of fast, cost-effective, and labor-efficient screening methods for germplasm. Near-Infrared Reflectance Spectroscopy (NIRS) addresses this by providing precise and rapid determination of crucial biochemical parameters. This study developed Modified Partial Least Squares (mPLS) regression-based NIRS prediction models using WinISI and 1D Convolutional Neural Networks (CNN) to enable high-throughput screening for moisture, ash, proteins, total soluble sugars (TSS), and phenols in Perilla germplasm. Calibration with WinISI involved mathematical treatments, optimizing for each trait: “2,6,6,1” for moisture, “3,4,4,1” for ash and TSS, “3,4,6,1” for protein, and “2,4,6,1” for phenols. The 1D CNN model, with lower mean absolute error (MAE), was further validated. External validation metrics, including RSQexternal, SEP(C), slope, bias, and RPD, assessed prediction accuracy. Comparative evaluation showed WinISI performed better for moisture prediction, while the 1D CNN model excelled in predicting ash, protein, TSS, and total phenol, highlighting the importance of model selection for specific traits. This rapid screening tool aids in identifying nutritionally dense Perilla genotypes, guiding targeted breeding efforts, and represents the first comparative mPLS and DL-based modeling using NIRS data for Perilla.

The charging protocol design is a control problem between charge time and capacity retention solved with numerous methods. However, the outcome is an optimal yet rigid charge protocol. Ever changing user behaviour limits the acceptability of one rigid optimal protocol to affect the growing market of LIBs - electric vehicles (EVs), embedded systems etc. It is imperative to redefine optimal charging by incorporating the user behaviour resulting in a dynamic charging strategy. We have formulated the personalized charging strategy problem as a Markov Decision Process (MDP). Q-learning and Deep Deterministic Policy Gradient (DDPG) method is used to solve the MDP. We then present a full spectrum of charging strategies based on perceived user requirement. Three representative charge protocols are demonstrated. The aggressive protocol can charge 77% SOC in ~15 minutes faster than MSCCCV (baseline). ~1.5 years of extra battery life is offered by the life-saver protocol which takes only 30 minutes more than MSCCCV to fully charge. The balanced protocol provides a quick boost and yet maintains a similar charge time and health as MSCCCV. We present an online methodology to retune the protocol on-device based on battery dynamics and user behavior. Finally, the claims are validated using real-world experiments. Note to Practitioners—This work was motivated by the problem of dynamic optimization of the battery-charging trajectory for each user of a mobile device or electric vehicle. The existing mechanism of an optimal charging problem revolves around finding a trade-off between charge time and battery health, not taking into account every user’s unique perspective. This offers only a fixed way of charging a device. This paper proposes to design an optimal charging protocol by taking user behaviour also into account. At each charging phase, the trajectory used for battery charging is optimized w.r.t expected charge time (user behaviour), battery health, and safety constraints of battery at that point of time. The method is trained to learn the battery ageing mechanism for individual user. Existing methodologies finds it tough to accommodate the process of optimal charging for fresh battery characteristics while also considering the constraints of an ageing battery. The proposed solution improves productivity because existing ways of optimizing require significant man-months. An automatic way of optimizing the charging protocol for each charging cycles is an efficient and reliable way to capture degradation as well as the battery-to-battery variability. The outcome of our work is a spectrum of charging protocols each optimized w.r.t battery characteristics and user expectations. Thus, each user can be catered to, with a unique charging protocol optimally designed for their immediate requirement. The practical limitation is to ensure the model converges during on-device retraining. The future direction of the work will be to incorporate more detailed battery model for optimizing the charging protocol.

Introduction: Patients living in high altitudes are often deprived of total knee arthroplasty (TKA) due to logistic reasons, economic, and social challenges in performing surgical procedures for management of knee pain. Surgical procedures in high-altitude dwellers have associated risk of deep venous thrombosis/pulmonary embolism (DVT/PE). In patients undergoing these procedures at lower altitudes, return to high altitudes can cause high-altitude pulmonary edema (HAPE). We share our experience of performing TKA in high-altitude dwellers by setting up a surgical camp at 11,000 feet. Methods: A retrospective assessment of patients undergoing total knee arthroplasty at a camp set up at 11,000 feet between 2014 and 2020 was undertaken. Follow-up data of patients which included clinical assessment by the Knee Society Score (KSS) and complications like DVT, infection, residual deformity, etc. were included in the study. Radiographic evaluation to look for evidence of implant loosening was also inculcated. Results: 132 patients (202 knee joints in 50 male and 82 female patients) underwent TKA during annual camps. The average follow-up of patients was 60 months. Mean pre-operative KSS was 38, which was increased to 83 at 1-year follow-up post-surgery. There was no evidence of DVT or superficial or deep infection in any patient in the post-operative period. Conclusion: With this study, we want to highlight that total knee arthroplasty can be safely performed at high altitudes and remote areas with limited health facilities. We believe it is a safer and more convenient prospect for the residents of high-altitude regions. Level of evidence: III.

Background and Purpose Hyperglycemia following acute ischemic stroke (AIS) is associated with adverse outcomes including, hemorrhagic conversion and increased length of stay; however, the impact of glycemic variability is largely unknown. This study aims to evaluate the effect of glycemic variability on discharge outcomes in patients treated with alteplase for AIS.Methods A retrospective review of ischemic stroke patients who presented within 4.5 hours from symptom onset and received alteplase was completed. Patients hospitalized for at least 48 hours were included. Glycemic variability was measured using J-index. Groups were defined by normal or abnormal J-indices. Logistic regression models were developed to determine odds ratios for select clinical characteristics, NIHSS score, mRS, and disposition at discharge.Results Of the 229 patients, 97 (42%) had an abnormal J-index. In the univariate analysis, abnormal J-index was associated with worse outcomes in terms of NIHSS score, mRS, and discharge disposition compared to a normal J-index. In the unadjusted multivariate analysis, abnormal J-index was associated with higher odds of unfavorable mRS (3-6) at discharge (OR 2.1; 95% CI 1.2 – 3.5, P= .009). In the adjusted multivariate analysis, patients with an abnormal J-index had higher odds of hemorrhagic transformation (OR 5.7; 95% CI 2.1 – 15.6, P< .0001). There was no difference in mortality.Conclusion Glycemic variability with abnormal J-index following AIS is associated with adverse functional outcomes at discharge and increased odds of hemorrhagic conversion in patients treated with alteplase. Additional studies validating glycemic variability indices post-ischemic stroke are needed to determine the full clinical impact.

Glossopharyngeal neuralgia (GPN) is a rare, debilitating neurologic condition characterized by intermittent pain radiating to the tongue, pharynx or ear that can be triggered by talking, swallowing or chewing. With an incidence of only 0.2-0.7 persons/100,000/year, the diagnosis is extremely challenging and often gets misdiagnosed and undiagnosed. Many treatment options have been tried by various clinicians in the past, such as medical therapy (antidepressants, opiods, antiepileptics, steroids, and membrane-stabilizing agents), nerve blocks with or without additives, gamma knife radiosurgery, radiofrequency ablation and microvascular decompression. Each of the aforementioned treatment modalities has its own merits and limitations. In the pursuit of delivering optimal treatment for pain relief in GPN and to improve patient's quality of life, we have explored the use of Botulinum toxin type A (BOTOX [Allegran]). BOTOX has been widely used in the treatment of chronic facial pain such as headaches, migraine, and trigeminal neuralgia. However, the use of BOTOX in the management of GPN has not been reported in the literature. Despite the small number of cases in our series, BOTOX therapy in GPN appears to be promising. However, further research with larger sample sizes and longer follow-up periods is needed to fully establish its efficacy and safety profile [ABSTRACT FROM AUTHOR]

A scalable platform to synthesize ultrathin heavy metals may enable high-efficiency charge-to-spin conversion for next-generation spintronics. Here, we report the synthesis of air-stable, epitaxially registered monolayer Pb underneath graphene on SiC (0001) by confinement heteroepitaxy (CHet). Diffraction, spectroscopy, and microscopy reveal that CHet-based Pb intercalation predominantly exhibits a mottled hexagonal superstructure due to an ordered network of Frenkel–Kontorova-like domain walls. The system’s air stability enables ex situspin torque ferromagnetic resonance (ST-FMR) measurements that demonstrate charge-to-spin conversion in graphene/Pb/ferromagnet heterostructures with a 1.5× increase in the effective field ratio compared to control samples.

Advancements in optical coherence control1–5have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6–8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9–11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson’s disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

Static three-dimensional (3D) cell culture has been demonstrated in ultralow attachment well plates, hanging droplet plates, and microtiter well plates with hydrogels or magnetic nanoparticles. Although it is simple, reproducible, and relatively inexpensive, thus potentially used for high-throughput screening, statically cultured 3D cells often suffer from a necrotic core due to limited nutrient and oxygen diffusion and waste removal and have a limited in vivo-like tissue structure. Here, we overcome these challenges by developing a pillar/perfusion plate platform and demonstrating high-throughput, dynamic 3D cell culture. Cell spheroids were loaded on the pillar plate with hydrogel by simple sandwiching and encapsulation and cultured dynamically in the perfusion plate on a digital rocker. Unlike traditional microfluidic devices, fast flow velocity was maintained within perfusion wells and the pillar plate was separated from the perfusion plate for cell-based assays. It was compatible with common lab equipment and allowed cell culture, testing, staining, and imaging in situ. The pillar/perfusion plate enhanced cell growth by rapid diffusion, reproducibility, assay throughput, and user friendliness in a dynamic 3D cell culture.

Purpose of Study: To compare the outcomes of minimally invasive and open techniques in the surgical management of dorsolumbar and lumbar spinal tuberculosis (STB). Methods: Skeletally mature patients with active STB involving thoracolumbar and lumbar region confirmed by radiology (X-ray, MRI) and histopathological examination were included. Healed and mechanically stable STB, patients having severe hepatic and renal impairment, coexisting spinal conditions such as ankylosing spondylitis and rheumatoid arthritis, and patients unwilling to participate were excluded from the study. The patients were divided in to two groups, group A consisted of patients treated by MIS techniques and group B consisted of patients treated by open techniques. All the patients had a minimum follow-up of 24 months. Results: A total of 42 patients were included in the study. MIS techniques were used in 18 patients and open techniques were used in 24 patients. On comparison between the two groups, blood loss (234 ml vs 742 ml), and immediate post-operative VAS score (5.26 vs 7.08) were significantly better in group A, whereas kyphotic correction (16° vs 33.25°) was significantly better in group B. Rest of the parameters such as duration of surgery, VAS score, ODI score and number of instrumented levels did not show significant difference between the two groups. Conclusion: MIS stabilization when compared to open techniques is associated with significant improvement in immediate post-operative VAS scores. The MIS approaches at 2-year follow-up have functional results similar to open techniques. MIS is inferior to open techniques in kyphosis correction and may be associated with complications.

Pseudomonas aeruginosais a leading cause of corneal infection both within India and globally, often causing a loss of vision. Increasing antimicrobial resistance among the bacteria is making its treatment more difficult. Preventing initial bacterial adherence to the host membrane has been explored here to reduce infection of the cornea. Synthetic peptides derived from human tetraspanin CD9 have been shown to reduce infection in corneal cells both in vitro, ex vivoand in vivo. We found constitutive expression of CD9 in immortalized human corneal epithelial cells by flow cytometry and immunocytochemistry. The synthetic peptides derived from CD9 significantly reduced bacterial adherence to cultured corneal epithelial cells and ex vivohuman cadaveric corneas as determined by colony forming units. The peptides also significantly reduced bacterial burden in a murine model of Pseudomonaskeratitis and lowered the cellular infiltration in the corneal stroma. Additionally, the peptides aided corneal wound healing in uninfected C57BL/6 mice compared to control mice. These potential therapeutics had no effect on cell viability or proliferation of corneal epithelial cells and have the potential to be developed as an alternative therapeutic intervention.

Fast modulation of optical signals that carry multidimensional information in the form of wavelength, phase or polarization has fueled an explosion of interest in integrated photonics. This interest however masks a significant challenge which is that independent modulation of multi-wavelength carrier signals in a single waveguide is not trivial. Such challenge is attributed to the longitudinal direction of guided-mode propagation, limiting the spatial separation and modulation of electric-field. Here, we overcome this using a single photonic element that utilizes active coherent (near) perfect absorption. We make use of standing wave patterns to exploit the spatial-degrees-of-freedom of in-plane modes and individually address elements according to their mode number. By combining the concept of coherent absorption in spatio-spectral domain with active phase-change nanoantennas, we engineer and test an integrated, reconfigurable and multi-spectral modulator operating within a single element. Our approach demonstrates for the first time, a non-volatile, wavelength-addressable element, providing a pathway for exploring the tunable capabilities in both spatial and spectral domains of coherent nanophotonics.

In the past decade, the proliferation of modern telecommunication technologies, including 5G, and the widespread adoption of the Internet-of-things (IoT) have led to an unprecedented surge in data generation and transmission. This surge has created an escalating demand for advanced signal processing capabilities. Microwave photonic (MWP) processors offer a promising solution to satisfy this unprecedented demand for data processing by capitalising on the high bandwidth and low latency achievable by optical systems. In this work, we introduce an integrated MWP processing unit for all-optical RF filtering using elemental antimony. We exploit the crystallisation dynamics of antimony to demonstrate a photonic leaky integrator, which is configured to operate as a first-order low-pass filter with a bandwidth of 300 kHz and ultra-compact footprint of 16 × 16 μm2. We experimentally demonstrate the implementation of such a filter as an envelope detector to demodulate an amplitude-modulated signal. Finally, a discussion on achieving bandwidth tunability is presented.

Myeloid cells are known to suppress antitumour immunity1. However, the molecular drivers of immunosuppressive myeloid cell states are not well defined. Here we used single-cell RNA sequencing of human and mouse non-small cell lung cancer (NSCLC) lesions, and found that in both species the type 2 cytokine interleukin-4 (IL-4) was predicted to be the primary driver of the tumour-infiltrating monocyte-derived macrophage phenotype. Using a panel of conditional knockout mice, we found that only deletion of the IL-4 receptor IL-4Rα in early myeloid progenitors in bone marrow reduced tumour burden, whereas deletion of IL-4Rα in downstream mature myeloid cells had no effect. Mechanistically, IL-4 derived from bone marrow basophils and eosinophils acted on granulocyte-monocyte progenitors to transcriptionally programme the development of immunosuppressive tumour-promoting myeloid cells. Consequentially, depletion of basophils profoundly reduced tumour burden and normalized myelopoiesis. We subsequently initiated a clinical trial of the IL-4Rα blocking antibody dupilumab2–5given in conjunction with PD-1/PD-L1 checkpoint blockade in patients with relapsed or refractory NSCLC who had progressed on PD-1/PD-L1 blockade alone (ClinicalTrials.gov identifier NCT05013450). Dupilumab supplementation reduced circulating monocytes, expanded tumour-infiltrating CD8 T cells, and in one out of six patients, drove a near-complete clinical response two months after treatment. Our study defines a central role for IL-4 in controlling immunosuppressive myelopoiesis in cancer, identifies a novel combination therapy for immune checkpoint blockade in humans, and highlights cancer as a systemic malady that requires therapeutic strategies beyond the primary disease site.

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.

Rapid urbanisation, especially in developing countries like India, has resulted in unplanned and haphazard urban expansion. With saturated urban cores, growth is observed in the peri-urban areas, resulting in severe challenges for urban planners. The present study aims to study the urban growth patterns of the fast-growing Mumbai Metropolitan Region (MMR) using the Landsat data from 1999 to 2019 and to evaluate the neighbourhood configurations’ effect on urban growth prediction. The urban area maps are classified using a maximum likelihood algorithm and are used along with the potential drivers to test three levels of neighbourhood considerations. The first model assumes no neighbourhood effect, the second incorporates the built-up pixels in the neighbourhood as an additional potential driver variable, and the third uses a Cellular Automata (CA). The CA model explores variations in neighbourhood types and sizes, distance decay and iterations to identify the optimal configuration. The results show an 89.44% increase in built-up areas over two decades (1999-2019). The urban growth prediction model testing reveals the importance of neighbourhood, with the first model without neighbourhood consideration giving the least accuracy (67%) while the inbuilt neighbourhood model gives better results (71%). However, the CA-based model with a 9 × 9 Moore neighbourhood, distance exponent β = 2 and two iterations give the highest accuracy (76%). The growth prediction shows a new wave of peri-urban growth in MMR, with overall urban areas increasing by 25% between 2019 and 2029 and 20% between 2029 and 2039. The results provide urban planners with a valuable tool for informed decision-making and promoting sustainable development.

Next generation sequencing based diagnosis has emerged as a promising tool for evaluating critically ill neonates and children. However, there is limited data on its utility in developing countries. We assessed its diagnostic rate and clinical impact on management of pediatric patients with a suspected genetic disorder requiring critical care. The study was conducted at a single tertiary hospital in Northern India. We analyzed 70 children with an illness requiring intensive care and obtained a precise molecular diagnosis in 32 of 70 probands (45.3%) using diverse sequencing techniques such as clinical exome, whole exome, and whole genome. A significant change in clinical outcome was observed in 13 of 32 (40.6%) diagnosed probands with a change in medication in 11 subjects and redirection to palliative care in two subjects. Additional benefits included specific dietary management (three cases), avoidance of a major procedure (one case) and better reproductive counseling. Dramatic therapeutic responses were observed in three cases with SCN1A, SCN2Aand KCNQ2-related epileptic encephalopathy. A delayed turn-around for sequencing results was perceived as a major limiting factor in the study, as rapid and ultra-rapid sequencing was not available. Achieving a precise molecular diagnosis has great utility in managing critically ill patients with suspected genetic disorders in developing countries.

The purpose of this study is to identify the factors that influence a consumer's decision to purchase apparel. The data was obtained using a structured questionnaire from consumers of various categories and analysed using grey analysis. It is evident that consumers prioritise price while purchasing apparel. They would rather be rational than emotional. It is obvious that the majority of respondents prefer branded over non-branded goods and consider malls to be the most suitable place for purchasing. Consumers also prefer to buy in stores with a diverse selection of apparel and a pleasant atmosphere. Deals and special offers play a big role in buying patterns of consumers. The study's main goal was to learn about buyers' perceptions of composed retail places, and the relationship between statistical parameters and buyer perceptions. The study suggests and employs a social mental model of purchasing power, which adds to the uniqueness of the research.

Regular inspection and monitoring of various critical equipment (porcelain insulators including pins or discs) available on power grid lines is required for several reasons, such as safe and uninterrupted transmission, avoiding shock accidents, and many more. In the earlier days, these inspection and maintenance activities were carried out manually by skilled experts. However, manual inspection is a challenging, dangerous and costly task. With the latest developments in the field of Artificial Intelligence and Unmanned Aerial Vehicles, several neural networks and feature-oriented research tools & techniques are introduced to identify broken components from the UAVs captured images. However, these benchmark research studies in this domain have several significant limitations, such as low generalization capability on different component types, inability to detect small-size components (pins & discs), and so on. To overcome these issues, the present research proposes a generalized model for identifying or classifying different small-size component types on the transmission lines. The proposed approach implements a deep learning pipeline involving several stages, including data preprocessing, quality assessment, augmentation, YOLOV5, and DETR for the target task. In addition, the current work also proposes a transfer learning strategy for improving classification accuracy and generalization capabilities. The performance assessment of the proposed approach and existing benchmark approaches on the porcelain insulator dataset validates the effectiveness and reliability of the proposed approach.

The implementation of the COVID-19 national lockdown announced suddenly in March 2020 in India provided a unique opportunity to capture real-time changes in business sentiments during episodes of unexpected and sudden disruptions. Using a logit-probability model to analyse data of this natural experiment showed that firms’ 6-months ahead sentiments for its financial condition worsened drastically during lockdown compared to firms surveyed immediately prior to the announcement. Further, smaller firms showed a relatively higher impact. We also find that firms perceive this as a relatively higher demand shock in terms of falling domestic sales post-lockdown whereas supply shocks are perceived to be on the downside. Lastly the mitigation strategy of firms involved reducing employment for unskilled workers and wages for skilled workers. This unique study gives insights not only about firms and their strategies but regarding appropriate policy choices during lockdown. The lessons are applicable for governments which imposed local lockdowns during the second wave and potential disruption for the expected third wave.

Introduction:TUBGCP6-related disorder is a known cause of autosomal recessive microcephaly and chorioretinopathy, which was originally recognized as a new syndrome based on unique ocular findings on a phenotypic overlap of microcephalic primordial short stature. Since the elucidation of its molecular mechanism, limited families have been published in literature and the disorder remains rare worldwide. Case Presentation:We present the first Indian family with an affected child and sibling fetus with microcephaly, dysmorphism, and agyria/pachygyria complex on brain imaging in both and short stature, intellectual disability, and visual impairment in proband. As for many patients with long diagnostic odysseys, this child also underwent multiple genomic tests. Genome sequencing through the Indian Undiagnosed Disease Program (I-UDP) confirmed the diagnosis in both proband and sibling fetus. Compound heterozygous variants were identified in TUBGCP6including an eleven base pair deletion (inherited from father) and 405 base pair large deletion (inherited from mother). Reverse phenotyping to confirm the ocular phenotype in proband confirmed TUBGCP6-related microcephaly and chorioretinopathy. We report third trimester microcephaly with ventriculomegaly and abnormal sulcation as part of the antenatal presentation for this condition. Conclusion:This case represents an Indian family with a seemingly obvious clinical diagnosis compounded by a long diagnostic odyssey and the first ever structural variant to be identified via whole genome sequencing in TUBGCP6in trans with an indel variant.

New developments in hardware-based ‘accelerators’ range from electronic tensor cores and memristor-based arrays to photonic implementations. The goal of these approaches is to handle the exponentially growing computational load of machine learning, which currently requires the doubling of hardware capability approximately every 3.5 months. One solution is increasing the data dimensionality that is processable by such hardware. Although two-dimensional data processing by multiplexing space and wavelength has been previously reported, the use of three-dimensional processing has not yet been implemented in hardware. In this paper, we introduce the radio-frequency modulation of photonic signals to increase parallelization, adding an additional dimension to the data alongside spatially distributed non-volatile memories and wavelength multiplexing. We leverage higher-dimensional processing to configure such a system to an architecture compatible with edge computing frameworks. Our system achieves a parallelism of 100, two orders higher than implementations using only the spatial and wavelength degrees of freedom. We demonstrate this by performing a synchronous convolution of 100 clinical electrocardiogram signals from patients with cardiovascular diseases, and constructing a convolutional neural network capable of identifying patients at sudden death risk with 93.5% accuracy.