Evaluation of bacteriological diagnosis of smear positive pulmonary tubreculosis under programme conditions in three districts in the context of DOTS implementation in India

Objective: To study the smear and culture positivity rates in pulmonary tuberculosis patients declared as smear positive in the districts of North Arcot (Tamil Nadu), Raichur (Karnataka) and Wardha (Maharashtra) in India in order to evaluate the diagnosis of pulmonary tuberculosis at the field level under programme conditions. Methods: Two specimens of sputum from each of 320 patients in North Arcot, 314 patients in Raichur and 302 patients from Wardha district, all of whom had been reported as smear-positive at the field level, were examined by smear and culture. Findings: The proportion of specimens found to be smear-negative was 4.7% in North Arcot and 5.7% in Raichur as against 38.7% in Wardha. The proportions of culture negative specimens were 5.7% and 6.3% respectively in North Arcot and Raichur, while it was 35.6% at Wardha. The difference in the smear and culture negativity between Wardha and the other two districts was highly significant. Conclusions: The study revealed an unacceptably high level of false positives in sputum smear microscopy in the Wardha district. This could be attributed to the absence of systematic and intensive training in smear examination consequent to the non-implementation of the DOTS strategy in this district and a high standard of training offered in the RNTCP implemented districts

Genome-wide mapping of Myc binding and gene regulation in serum-stimulated fibroblasts

The transition from quiescence to proliferation is a key regulatory step that can be induced by serum stimulation in cultured fibroblasts. The transcription factor Myc is directly induced by serum mitogens and drives a secondary gene expression program that remains largely unknown. Using mRNA profiling, we identify close to 300 Myc-dependent serum response (MDSR) genes, which are induced by serum in a Myc-dependent manner in mouse fibroblasts. Mapping of genomic Myc-binding sites by ChIP-seq technology revealed that most MDSR genes were directly targeted by Myc, but represented a minor fraction (5.5%) of all Myc-bound promoters (which were 22.4% of all promoters). Other target loci were either induced by serum in a Myc-independent manner, were not significantly regulated or were negatively regulated. MDSR gene products were involved in a variety of processes, including nucleotide biosynthesis, ribosome biogenesis, DNA replication and RNA control. Of the 29 MDSR genes targeted by RNA interference, three showed a requirement for cell-cycle entry upon serum stimulation and 11 for long-term proliferation and/or survival. Hence, proper coordination of key regulatory and biosynthetic pathways following mitogenic stimulation relies upon the concerted regulation of multiple Myc-dependent genes

Epidermal Notch1 recruits RORγ+ group 3 innate lymphoid cells to orchestrate normal skin repair

Notch has a well-defined role in controlling cell fate decisions in the embryo and the adult epidermis and immune systems, yet emerging evidence suggests Notch also directs non-cell-autonomous signalling in adult tissues. Here, we show that Notch1 works as a damage response signal. Epidermal Notch induces recruitment of immune cell subsets including RORγ + ILC3s into wounded dermis; RORγ + ILC3s are potent sources of IL17F in wounds and control immunological and epidermal cell responses. Mice deficient for RORγ + ILC3s heal wounds poorly resulting from delayed epidermal proliferation and macrophage recruitment in a CCL3-dependent process. Notch1 upregulates TNFα and the ILC3 recruitment chemokines CCL20 and CXCL13. TNFα, as a Notch1 effector, directs ILC3 localization and rates of wound healing. Altogether these findings suggest that Notch is a key stress/injury signal in skin epithelium driving innate immune cell recruitment and normal skin tissue repair

Chemotaxis: a feedback-based computational model robustly predicts multiple aspects of real cell behaviour

The mechanism of eukaryotic chemotaxis remains unclear despite intensive study. The most frequently described mechanism acts through attractants causing actin polymerization, in turn leading to pseudopod formation and cell movement. We recently proposed an alternative mechanism, supported by several lines of data, in which pseudopods are made by a self-generated cycle. If chemoattractants are present, they modulate the cycle rather than directly causing actin polymerization. The aim of this work is to test the explanatory and predictive powers of such pseudopod-based models to predict the complex behaviour of cells in chemotaxis. We have now tested the effectiveness of this mechanism using a computational model of cell movement and chemotaxis based on pseudopod autocatalysis. The model reproduces a surprisingly wide range of existing data about cell movement and chemotaxis. It simulates cell polarization and persistence without stimuli and selection of accurate pseudopods when chemoattractant gradients are present. It predicts both bias of pseudopod position in low chemoattractant gradients and-unexpectedly-lateral pseudopod initiation in high gradients. To test the predictive ability of the model, we looked for untested and novel predictions. One prediction from the model is that the angle between successive pseudopods at the front of the cell will increase in proportion to the difference between the cell's direction and the direction of the gradient. We measured the angles between pseudopods in chemotaxing Dictyostelium cells under different conditions and found the results agreed with the model extremely well. Our model and data together suggest that in rapidly moving cells like Dictyostelium and neutrophils an intrinsic pseudopod cycle lies at the heart of cell motility. This implies that the mechanism behind chemotaxis relies on modification of intrinsic pseudopod behaviour, more than generation of new pseudopods or actin polymerization by chemoattractant

The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate

Bi-phasic or diauxic growth is often observed when microbes are grown in a chemically defined medium containing two sugars (for example glucose and lactose). Typically, the two growth stages are separated by an often lengthy phase of arrested growth, the so-called lag-phase. Diauxic growth is usually interpreted as an adaptation to maximise population growth in multi-nutrient environments. However, the lag-phase implies a substantial loss of growth during the switch-over. It therefore remains unexplained why the lag-phase is adaptive. Here we show by means of a stochastic simulation model based on the bacterial PTS system that it is not possible to shorten the lag-phase without incurring a permanent growth-penalty. Mechanistically, this is due to the inherent and well established limitations of biological sensors to operate efficiently at a given resource cost. Hence, there is a trade-off between lost growth during the diauxic switch and the long-term growth potential of the cell. Using simulated evolution we predict that the lag-phase will evolve depending on the distribution of conditions experienced during adaptation. In environments where switching is less frequently required, the lag-phase will evolve to be longer whereas, in frequently changing environments, the lag-phase will evolve to be shorter

Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping

We present an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or motion-captured data and represented as sequences of 3D poses. Given the motion on each joint in the pose at each time step extracted from 3D pose sequences, we hierarchically pool these joint motions in a bottom-up manner in the encoder, following the kinematic chains in the human body. We also constrain the latent embeddings of the encoder to contain the space of psychologically-motivated affective features underlying the gaits. We train the decoder to reconstruct the motions per joint per time step in a top-down manner from the latent embeddings. For the annotated data, we also train a classifier to map the latent embeddings to emotion labels. Our semi-supervised approach achieves a mean average precision of 0.84 on the Emotion-Gait benchmark dataset, which contains both labeled and unlabeled gaits collected from multiple sources. We outperform current state-of-art algorithms for both emotion recognition and action recognition from 3D gaits by 7%--23% on the absolute. More importantly, we improve the average precision by 10%--50% on the absolute on classes that each makes up less than 25% of the labeled part of the Emotion-Gait benchmark dataset.Comment: In proceedings of the 16th European Conference on Computer Vision, 2020. Total pages 18. Total figures 5. Total tables