A prospective analysis of sleep deprivation and disturbance in surgical patients

Introduction: Sleep deprivation has a potentially deleterious effect on postoperative recovery. The aim of our prospective study was to identify the factors contributing to postoperative sleep deprivation and disturbance in order to recommend improvements in postoperative care. Methods: 102 consecutive patients attending for elective general and orthopaedic surgery were interviewed preoperatively (baseline) and postoperatively on their duration of sleep, number of wakenings during the night, factors contributing to sleep loss and the use of analgesia and night sedation. Results: Patients woke up a median of 5 times in the first postoperative night compared to a median of 3 times preoperatively (p = 0.01). Pain was the predominant factor preventing sleep, affecting 39% of patients preoperatively and 48% of patients on the first postoperative day. Other factors included noise from other patients and nursing staff, and using the toilet. Analgesia was taken by more than 90% of patients in the first two days, this number gradually reducing over the postoperative period. On the other hand, in the first two postoperative days, only about 5% of patients had night sedation. Discussion and conclusions: Apart from highlighting the need for effective pain management postoperatively, we believe that our study supports the drive towards single bed bays, where steps can be taken to minimize the impact of environmental factors on sleep

HYBRIDdb: a database of hybrid genes in the human genome

<p>Abstract</p> <p>Background</p> <p>Hybrid genes are candidate risk factors for human tumors by inducing mutation, translocation, inversion, or rearrangement of genes. The occurrence of hybrid genes may also have given rise to new transcripts during hominid evolution.</p> <p>Description</p> <p>HYBRIDdb is a database of hybrid genes in humans. This system encompasses the bioinformatics analysis of mRNA, EST, cDNA, and genomic DNA sequences in the INDC databases, and can be used to identify hybrid genes. We searched for hybrid genes among the 28,171 genes listed in the NCBI database, and analyzed their structural patterns in the human genome. The 2,344 gene pairs were detected as hybrid forms of transcriptional products. We classified the hybrid genes into two groups: chromosomal-mediated translocation fusion transcripts and transcription-mediated fusion transcripts.</p> <p>Conclusion</p> <p>The HYBRIDdb database will provide genome scientists with insight into potential roles for hybrid genes in human evolution and disease.</p

Observation of a strongly ferromagnetic spinor Bose-Einstein condensate

We report the observation of strongly ferromagnetic $F=1$ spinor Bose-Einstein condensates of $^7$Li atoms. The condensates are generated in an optical dipole trap without using magnetic Feshbach resonances, so that the condensates have internal spin degrees of freedom. Studying the non-equilibrium spin dynamics, we have measured the ferromagnetic spin interaction energy and determined the $s$-wave scattering length difference among total spin $f$ channels to be $a_{f=2}-a_{f=0} =-18(3)$ Bohr radius. This strong collision-channel dependence leads to a large variation in the condensate size with different spin composition. We were able to excite a radial monopole mode after a spin-flip transition between the $|m_F=0\rangle$ and $|m_F=1\rangle$ spin states. From the experiments, we estimated the scattering length ratio $a_{f=2}/a_{f=0}=0.27(6)$, and determined $a_{f=2}$ = 7(2) and $a_{f=0}$ = 25(5) Bohr radii, respectively. The results indicate the spin-dependent interaction energy of our system is as large as 46$\%$ of the condensate chemical potential

WhisperX: time-accurate speech transcription of long-form audio

Large-scale, weakly-supervised speech recognition models, such as Whisper, have demonstrated impressive results on speech recognition across domains and languages. However, their application to long audio transcription via buffered or sliding window approaches is prone to drifting, hallucination & repetition; and prohibits batched transcription due to their sequential nature. Further, timestamps corresponding each utterance are prone to inaccuracies and word-level timestamps are not available out-of-the-box. To overcome these challenges, we present WhisperX, a time-accurate speech recognition system with word-level timestamps utilising voice activity detection and forced phoneme alignment. In doing so, we demonstrate state-of-the-art performance on long-form transcription and word segmentation benchmarks. Additionally, we show that pre-segmenting audio with our proposed VAD Cut & Merge strategy improves transcription quality and enables a twelvefold transcription speedup via batched inference. The code is available open-source

Emission of Spin-correlated Matter-wave Jets from Spinor Bose-Einstein Condensates

We report the observation of matter-wave jet emission in a strongly ferromagnetic spinor Bose-Einstein condensate of $^7$Li atoms. Directional atomic beams with $|{F=1,m_F=1}\rangle$ and $|{F=1,m_F=-1}\rangle$ spin states are generated from $|{F=1,m_F=0}\rangle$ state condensates, or vice versa. This results from collective spin-mixing scattering events, where spontaneously produced pairs of atoms with opposite momentum facilitates additional spin-mixing collisions as they pass through the condensates. The matter-wave jets of different spin states ($|{F=1,m_F=\pm1}\rangle$) can be a macroscopic Einstein-Podolsky-Rosen state with spacelike separation. Its spin-momentum correlations are studied by using the angular correlation function for each spin state. Rotating the spin axis, the inter-spin and intra-spin momentum correlation peaks display a high contrast oscillation, indicating collective coherence of the atomic ensembles. We provide numerical calculations that describe the experimental results at a quantitative level and can identify its entanglement after 100~ms of a long time-of-flight.Comment: 13 pages(6 main text, 7 supplemental material), 12 figure

TIM: a time interval machine for audio-visual action recognition

Diverse actions give rise to rich audio-visual signals in long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of events and distinct labels. We address the interplay between the two modalities in long videos by explicitly modelling the temporal extents of audio and visual events. We propose the Time Interval Machine (TIM) where a modality-specific time interval poses as a query to a transformer encoder that ingests a long video input. The encoder then attends to the specified interval, as well as the surrounding context in both modalities, in order to recognise the ongoing action. We test TIM on three long audio-visual video datasets: EPIC-KITCHENS, Perception Test, and AVE, reporting state-of-the-art (SOTA) for recognition. On EPICKITCHENS, we beat previous SOTA that utilises LLMs and significantly larger pre-training by 2.9% top-1 action recognition accuracy. Additionally, we show that TIM can be adapted for action detection, using dense multi-scale interval queries, outperforming SOTA on EPIC-KITCHENS-100 for most metrics, and showing strong performance on the Perception Test. Our ablations show the critical role of integrating the two modalities and modelling their time intervals in achieving this performance. Code and models at: https://github.com/JacobChalk/TIM